Morningstar ratings and fund performance blake morey

J O U R N A L OF FINANCIAL A N D OUANTITATIVE ANALYSIS VOL. 35, NO. 3, SEPTEMBER 2000
COPYRIGHT 2000. SCHOOL OF BUSINESS ADMINISTRATION. UNIVERSITY OF WASHINGTON. SEATTLE. WA 98195

Momingstar Ratings and Mutual Fund
Performance
Christopher R. Blake ancj Matthew R. Morey*

Abstract
This study examines the Momingstar rating system as a predictor of mutual fund perfor-
mance for U.S. domestic equity funds. We also compare the predictive abilities of the
Momingstar rating system with those of altemative predictors. The results indicate find-
ings that are robust across different samples, ages and styles of funds, and performance
measures. First, low ratings from Momingstar generally indicate relatively poor future
performance. Second, there is little statistical evidence that Momingstar's highest-rated
funds outperform the next-to-highest and median-rated funds. Third, Momingstar ratings,
at best, do only slightly better than the altemative predictors in forecasting future fund
performance.

I. Introduction
In recent years, increasing attention has been paid to the persistence of mu-
tual fund performance in the finance literature.' Yet, to date, there has been con-
siderably less attention devoted to the predictive abilities of the Momingstar five-
star mutual fund rating service that many investors use as a guide in their mutual
fund selections. This study attempts to fill that void by examining the ability of
the Momingstar ratings to predict both unadjusted and risk-adjusted retums in
comparison with performance metrics common in the performance literature.
The question of whether Momingstar ratings predict out-of-sample perfor-
mance is an important one, given that several studies in the performance literature
have documented that new cash flows from investors are related to past perfor-
mance ratings (see, e.g., Sirri and Tufano (1998) and Gruber (1996)). In fact,
there is evidence that high-rated funds experience cash inflows that are far greater
* Blake, Graduate School of Business Administration, Fordham University, 113 West 60th St., New
York, NY 10023; Morey, Lubin School of Business, Pace University, 1 Pace Plaza, New York, NY
10038. Morey acknowledges financial support from the Economic and Pension Research Department
of TIAA-CREF. We thank Will Goetzmann and Charles Trzcinka for data and Stephen Brown (the
editor), Edwin Elton, Steve Foerster, Doug Fore, Martin Gruber, Mark Hulbert, Zoran Ivkovid (the
referee), Richard C. Morey, Derrick Reagle, Emily Rosenbaum, H. D. Vinod/ Mark Warshawsky,
and .seminar participants at the Securities and Exchange Commission and the 1999 European Finance
Association Meetings (Helsinki) for helpful comments and suggestions.
'For example, see Hendricks, Patel, and Zeckhauser (1993), Goetzmann and IbboLson (1994),
Malkiel (1995), Brown and Goetzmann (1995), Elton, Gruber, and Blake (1996a), and Carhart (1997).
451

452 Journal of Financial and Quantitative Analysis

in size than the cash outflows experienced by low-rated funds (see, e.g., Sirri and
Tufano (1998) and Goetzmann and Peles (1997)). Hence, examining performance
across funds grouped by Momingstar rankings will indicate if these cash flows are
justified by subsequent relative performance.
As evidence of the importatice of the Momingstar five-star rating service
(where a five-star rating is the best and a one-star rating is the worst), consider
a recent study, reported in both the Boston Globe and The Wall Street Journal,^
which found that 97% of the money flowing into no-load equity funds between
January and August 1995 was invested into funds that were rated as five- or four-
star funds by Momingstar, while funds with less than three stars suffered a net
outflow of funds during the same period. Moreover, the heavy use of Momingstar
ratings in mutual fund advertising suggests that mutual fund companies believe
that investors care about Momingstar ratings. Indeed, in some cases, the only
mention of retum performance in the mutual fund advertisement is the Mom-
ingstar rating. Finally, the importance of Momingstar ratings has been under-
scored by some recent high-profile publications (e.g., Blume (1998) and Sharpe
(1998)) that have investigated the underlying properties of the Momingstar rating
system.
Despite its importance, there is, to our knowledge, only one extant academic
study on the predictive abilities of the Momingstar ratings service. Khorana and
Nelling (1998) examine the question of persistence of the Momingstar ratings
and compare the Momingstar ratings from a group of funds in December 1992
to the June 1995 ratings of those same funds. Although they find evidence of
persistence, in that high-rated funds are still high rated and low-rated funds are
still low rated, there are a number of problems with the study. First, there is a
survivorship bias problem, since the funds were selected at the end of the sample
period rather than at the beginning. Hence, any fund that had merged, liquidated,
or changed its name between the beginning and ending of the sample period was
not included. Second, because Momingstar uses a 10-year risk-adjusted retum
as a major component of its ratings, and because there are only two and one-
half years of data between the beginning and end of the Khorana and Nelling
sample, the ratings are based on overlapping data. Consequently, the findings of
persistence in the ratings are endemic to the data. Finally, Khorana and Nelling
only examine performance persistence as measured by Momingstar ratings; they
do not examine how well Momingstar ratings predict more standard measures of
performance.^
In this paper, we examine whether the Momingstar five-star system has any
predictive power for the future performance of funds. Our data and methodology
are sensitive to the following key issues in mutual fund research.

^Charles Jaffe, "Rating the Raters: Flaws Found in Each Service," Boston Globe, Aug. 27, 1995,
p. 78. The same survey was also reported by Karen Damato, "Momingstar Edges Toward One-Year
Ratings," The Wall Street Journal, April 5, 1996.
^It should be noted that Momingstar reports an in-house study, conducted by Laura Lallos in 1997,
in which 45% of the five-star funds in 1987 receive five stars in 1997. However, no other comparisons
are provided and few details of the study are reported.

Blake and Morey 453

i) We use a mutual fund data set generated at the time the funds were actually
rated by Momingstar and then follow the out-of-sample performance of
these funds. This methodology allows us to circumvent the well-known
survivorship bias problem described by Brown, Goetzmann, Ibbotson, and
Ross (1992), Elton, Gruber, and Blake (1996b), and others.
ii) We adjust retums for front-end and deferred loads, because the Momingstar
rating system also adjusts for loads.
iii) We compare the predictive abilities of the Momingstar ratings with those of
altemative predictors: in-sample historical average monthly retums, one-
and four-index in-sample alphas, and in-sample Sharpe (1966) ratios.
iv) We examine out-of-sample one-, three-, and five-year horizons, so that we
can give both short- and long-term analyses of the predictive abilities of
Momingstar ratings and the altemative predictors. Moreover, these time
horizons are consistent with the historical retums that prospective investors
are often provided when considering a mutual fund.
v) We examine the predictive abilities of the Momingstar ratings and the alter-
native predictors at different times to observe how well they predict in up
and down markets.
vi) We address the issue of performance predictability by separating domestic
equity funds according to investment style (i.e., aggressive growth, equity-
income, growth, growth-income, and small company funds) at the time they
were rated (see Brown (1999), Brown and Goetzmann (1997), Elton, Gm-
ber. Das, and Hlavka (1993), and Goetzmann and Ibbotson (1994)).
vii) We explore whether the age of a fund affects performance predictability by
separating funds into young (three to five years), middle-aged (five to 10
years), and seasoned (10 years or more) groups.
viii) We measure out-of-sample performance using several well-known perfor-
mance metrics, including the Sharpe ratio, mean monthly excess retums, a
modified version of Jensen's alpha (1968), and a four-index alpha (Elton,
Gruber, and Blake (1996a)).
ix) We analyze the results using parametric and non-parametric tests.
The paper is organized as follows. Section II describes the data and relates
how the funds were chosen, how Momingstar calculates their ratings, and how
the retums data were collected and calculated. Section III describes the method-
ology of the paper. Section IV presents the Momingstar rating results. Section V
presents the altemative predictor results, and Section VI provides the conclusion.

II. Data

A. Sample Groups and Fund Selection Criteria

We examine two broad sample groups in this study. For simplicity, we term
these samples seasoned funds 1992-1997 and complete funds 1993.


1. Seasoned Funds 1992-1997

For the first sample group, we use the beginning-of-the-year Momingstar
On-Disk or Principia programs from 1992 to 1997 to select mutual funds.** We
use the beginning-of-the-year disks to simplify the data so that we always examine
calendar years. Moreover, we start at the beginning of the year 1992 since this
corresponds to the first beginning-of-the-year On-Disk program.'
By using the actual Momingstar disks, we know all the funds available to
investors selecting funds based on Momingstar ratings at the time of the Mom-
ingstar evaluation, thus circumventing any possible survivorship bias problems.
Data prior to the beginning of the On-Disk program are available from Mom-
ingstar on a proprietary basis, however, these data include only the surviving
funds. Funds that were rated at the time of the Momingstar rating that were
merged or liquidated at some later date are not available.^ Since the use of such
data would introduce a severe survivorship bias, they are not used in our study.
From the beginning-of-the-year disks we then select funds based on three cri-
teria. First, we select only "domestic equity" funds as identified by Momingstar's
"Investment Class." From the domestic equity funds, we then select all funds
within each of the following five Momingstar "investment objectives" (styles):
aggressive growth, equity-income, growth, growth and income, and small com-
pany. This allows us to examine whether there is a "style effect" on fund per-
formance predictability. It is important to note that the designation for the "in-
vestment objective" is determined by Momingstar, usually based on the wording
in the fund's prospectus. However, in some cases, Momingstar may give a fund
an investment objective different from that implied by the fund's name or by the
fund's prospectus if Momingstar determines that the fund invests in a way that is
inconsistent with the wording in its prospectus.
Since we examine the out-of-sample performance, we also examine if the
funds retain their classifications by Momingstar in the out-of-sample periods. In
every sample examined, at least 85% ofthe funds retain their style classification at
the end of the sample period.^ Hence, according to Momingstar, the vast majority
of funds do not change their style of management.
Second, each fund had to have at least 10 years of retums at the time it
was rated by Momingstar; e.g., funds rated by Momingstar in January 1993 had
to have retums data starting from, at the latest, January 1983. We use the 10-
year cutoff because the 10-year in-sample period utilizes Momingstar's base-line
rating system. As stated earlier, Momingstar provides each mutual fund with a
one- to five-star summary rating. To obtain this summary rating, Momingstar
takes a weighted average of the three-, five-, and 10-year risk-adjusted retums,
where the weights are 20% on the three-year retum, 30% on the five-year retum.

••These corre.spond to the January 1992 On-Disk, January 1993 On-Disk, January 1994 On-Disk,
January 1995 On-Disk, January 1996 On-Disk, and the January 1997 Principia. (In October 1996
On-Disk changed to Principia.)
'The On-Disks begin in October 1991.
*We thank Peter Carrillo of Momingstar for this point.
^To obtain this percentage, we examined only the funds in the sample that did not merge or liqui-
date during the out-of-sample period.

Blake and Morey 455

and 50% on the 10-year retum. Due to its importance in Momingstar's rankings,
we used the 10-year time period as a criterion in selecting funds.
Third, funds had to be open at the time they were rated by Momingstar. Any
fund that was closed to new investors at the time of the rating was excluded from
our analysis to maintain a sample of funds that could actually be invested in at the
time of the ratings.*

2. Complete 1993 Sample
The above sample group excludes many Momingstar-rated funds simply be-
cause of our criterion that funds must have 10 years of in-sample data at the time
they are rated. While their base-line rating system uses a combination of the
three-, five-, and 10-year retums, Momingstar still rates funds with less than 10
years of retums. So long as a fund has at least three years of retums history, it can
receive a summary star rating from Momingstar. Funds with three-five years of
retums history are rated using a system that puts a 100% weighting on their three-
year past performance; funds with five-10 years of retums history are rated using
a system that puts a 40% weighting on the three-year retum and a 60% weighting
on the five-year retum.
By excluding younger funds, we miss out on another interesting aspect of
the Momingstar rating system. Since younger funds are rated only on short-term
retums (i.e., the three-year retum) whereas older funds are rated on a combination
of the three-, five-, and 10-year retums, the younger fund ratings are particularly
sensitive to the overall performance of the market. In a bull market (as in the
late 1990s), young equity funds could receive higher ratings not because of better
short-term performance, but rather because the rating system only evaluates them
during a time when the market is doing exceptionally well.^ This could alter the
predictive ability of the ratings.
The problem with including younger funds in our first sample group is that
the number of funds to examine is onerous. As a tradeoff, we create another
sample in which we include virtually all open aggressive growth, equity-income,
growth, growth-income, and small company funds that were rated by Momingstar
in January 1993.'" By using the 1993 data, we only have to examine the out-
of-sample performance of 635 funds as opposed to well over 2000 funds if we
were to use the 1996 or 1997 On-Disk/Principia programs. Furthermore, by using
January 1993 rated funds, we are still able to follow out-of-sample performance
out to five years.
In summary, the complete funds 1993 sample includes all open funds rated
by Momingstar and listed as aggressive growth, equity-income, growth, growth-
income, or small company. Hence, this includes young funds (between three and
five years of retum history at the time they were rated), middle-aged funds (be-

*The number of closed funds that met our other criteria was as follows: January 1992 sample:
11 funds; January 1993 sample: 11 funds; January 1994 sample: 19 funds; January 1995 sample: 19
funds; January 1996 sample: 28 funds; January 1997 sample: 37 funds.
'Blume (1998), in a study utilizing only 1996 data, provides some evidence that there is a relatively
high percentage of young funds that are classified as five-star or one-star funds.
'"Twenty-four funds met our other criteria, yet were listed a.s closed funds in January 1993; we
excluded them from the sample.


tween five and 10 years of historical retums at the time they were rated), and
seasoned funds (10 or more years of retums at the time they were rated).
As with the seasoned funds samples, we also examine the number of funds
that change their investment objective in the out-of-sample periods. Similar to the
seasoned funds sample, at least 85% of the funds do not change their Momingstar
investment objective over the course of the out-of-sample period."

B. Problem Funds

To examine the out-of-sample forecast ability of Momingstar ratings, we
obtain the out-of-sample monthly retums of these funds. For a majority of the
funds, obtaining the out-of-sample retums is simply a matter of following the
previously rated fund. However, because a minority of funds have either gone
through a name change, a merger, a combination of both, or because they have
liquidated, identifying out-of-sample retums for those funds is more complicated.
We describe how we handle these problematic funds.
We use the Momingstar data'^ and The Wall Street Journal to identify the
name changes. We then simply use the newly named fund's retums as the out-of-
sample retums.
For the merged funds, we used the Momingstar data and The Wall Street
Journal to ascertain the month of the fund merger. However, when these two
sources did not provide the necessary information, we called the individual mu-
tual fund companies. Once the merger month was identified, we then collected
the out-of-sample retums by the following procedure. First, until the fund merges,
we simply use the out-of-sample retums of the fund in question. After the fund
merges into its partner fund, we assume the investor randomly reinvests into one
of the other surviving funds with the same investment objective as the merged
fund in our sample. Hence, the out-of-sample retums from the merger month
onward are equally-weighted monthly averages of the retums of all the other sur-
viving funds in our sample with the same investment objective as the merged
fund. The assumption of random reinvestment into any surviving fund regardless
of its ranking may at first blush seem irrational, given that investors should prefer
superior funds. But we are examining Momingstar predictability, not only for su-
perior, but also for inferior performance. Forcing random reinvestment into only
high-ranked funds could bias the predictability results. Furthermore, an investor
may be interested in using Momingstar rankings not only for investment in supe-
rior funds, but also for avoiding investment in inferior funds. (Nevertheless, we
did examine the results obtained by assuming an investor randomly chose a sur-
viving fund from those rated only three stars or better; the results were virtually
identical.)'^

" As with our seasoned funds sample, we examined only the funds in the sample that did not merge
or liquidate during the out-of-sample period to obtain this percentage.
'^The Momingstar On-Disk and Principia disks (after 1993) each provide a list of funds that have
recently undergone name changes, mergers, and liquidations.
'^An additional issue in using our "random reinvestment" assumption is that an investor may incur
capital gains or losses when closing out an investment in a merged fund and then investing in another
fund, inducing a tax effect. However, even for surviving funds, potential tax effects exist because
funds must by law distribute all income distributions and intemally generated capital gains to their

Blake and Morey 457

For the liquidated funds, we first determined when the fund was liquidated.
Again, this information was obtained from Momingstar or The Wall Street Jour-
nal. As with the merged funds, from the month of liquidation and onward, we
assume the investor randomly re-invests in the current sample of funds with the
same investment objective as the merged fund.

C. Momingstar Ratings

To calculate its ratings, Momingstar first classifies funds into one of four
categories: domestic equity, foreign equity, municipal bond, and taxable bond.'''
The ratings are then based upon an aggregation of the three-, five-, and 10-year
risk-adjusted retum for funds with 10 years or more of retum history, three- and
five-year risk-adjusted retums for funds with five to less than 10 years of retum
data, and three-year risk-adjusted retums for funds with three to less than five
years of retum data. To calculate the risk-adjusted retum, Momingstar first calcu-
lates a load-adjusted retum for the fund by adjusting the retums for expenses such
as 12b-l fees, management fees, and other costs automatically taken out of the
fund, and then by adjusting for front-end and deferred loads.'^ Next, Momingstar
calculates a "Momingstar retum" in which the expense- and load-adjusted excess
is retum divided by the higher of two variables: the excess average retum of the
fund category (domestic stock, intemational stock, taxable bond, or municipal
bond) or the average 90-day U.S. T-bill rate,

(Expense- and Load-Adjusted Retum on the Fund - T-Bill)
max[{Average Category Retum - T-Bill), T-Bill]

Momingstar divides through by one of these two variables to prevent distortions
caused by having low or negative average excess retums in the denominator of
equation (1). Such a situation might occur in a protracted down market.'^
investors, and, even if an investor chooses to automatically reinvest a distribution back into the fund,
it is a taxable event (unles.s the fund is held in a tax-sheltered plan such as an IRA or Keogh plan).
This study (and most other extant studies on fund performance) analyzes pre-tax retums, so any tax
effects of our reinvestment assumption do not affect our results. An altemative approach that does not
have the potential tax effect of our reinvestment approach for merged funds is the "follow-the-money"
approach introduced in Elton, Gruber, and Blake (1996b), where a merged fund's retums are spliced
to its "merge partner" fund's retums to form a complete time series. (The investor essentially stays
invested without having to close out and then reinvest.) But because of the way we calculate our out-
of-sample performance alpha.s for disappearing funds (see Section III for details), we would require a
complete in-sample time series of retums for the "merge partner" fund, and in some cases the partner
fund did not exist long enough to obtain such a series.
'••originally Momingstar used only three categories: domestic equity, municipal bond, and taxable
bond. The foreign equity funds were placed in the domestic equity category. The foreign equity cat-
egory was started in 1996. The category definitions we describe in this paper are from Momingstar's
1998 manual.
"Blume (1998), pp. 4-5, provides an excellent description of how Momingstar accounts for loads
in the Momingstar retums. The load adjustment process is the following. Assume L is the load
adjustment. If there is no load of any type, then L is equal to 1. If there is a load, L is less than one;
e.g., a 4% front-end load would make L equal to 0.96. The load-adjusted retum is then equal to the
return of the fund times L Note that the front-end load is always assumed to the be the maximum
possible load. The deferred load adjustment is reduced as the holding period is increased. In Section
II, we explain in more detail how we adjust the retum data for loads.
'^Principia Manual, p. 97.


Momingstar then calculates a "Momingstar risk" measure, which is calcu-
lated differently from traditional risk measures, such as beta and standard devia-
tion that both see greater-than and less-than-expected retums as added volatility.
Momingstar believes that most investors' greatest fear is losing money, which
Momingstar defines as underperforming the risk-free rate of retum an investor
can eam from the 90-day Treasury bill. Hence, their risk measure only focuses
on downside risk.'' To calculate risk, Momingstar plots monthly retums in re-
lation to T-bill retums, adds up the amounts by which the fund trails the T-bill
retum each month, and then divides that total by the time horizon's total number
of months. This number, the average monthly underperformance statistic, is then
compared with those of other funds in the same broad investment category to as-
sign the risk scores. The resultant Momingstar risk score expresses how risky the
fund is relative to the average fund in its category.'^
To calculate a fund's summary star rating, Momingstar calculates the three-,
five-, and 10-year Momingstar retum and risk. For each time horizon, the Mom-
ingstar risk scores are then subtracted from the Momingstar retum scores. The
three numbers (one for each time horizon) are then given subjective weights."
The three-year number receives a 20% weighting, the five-year a 30% weighting,
and the 10-year a 50% weighting. As stated above, in the case of young funds
(funds with three to less than five years of retum data), the three-year number re-
ceives a 100% weighting; in the case of middle-aged funds (funds with five to less
than 10 years of retum data), the three-year number receives a 40% weighting and
the five-year number receives a 60% weighting. With these weights, Momingstar
calculates the weighted average of the numbers. The resulting number is then
plotted along a bell curve to determine the fund's star rating. If the fund scores
in the top 10% of its broad investment category, it receives a rating of five stars;
if the fund falls in the next 22.5%, it receives four stars; if it falls in the middle
35%, it receives three stars; if it lies in the next 22.5%, the fund receives two stars,
and if it is in the bottom 10%, it receives one star. Momingstar, with a few minor
exceptions, has used this same summary rating system throughout its history.^°
Several features of the data should be noted.^'
i) For the seasoned fund subsamples, the number of funds in each sample
grows. This is not surprising, since with each year the number of funds
that meet the criteria grow.

'^Focusing only on downside risk is neither unique to Momingstar nor new; it was explored by
Markowitz (1959) and incorporated into an asset-pricing model by Bawa and Lindenberg (1977).
"principia Manual, p. 98.
"Morey and Morey (1999) present a methodology that endogenously determines these weights.
^"The Momingstar technical staff verified this point. See Blume (1998), p. 3, for more on this
issue.
^' Detailed tables describing the breakdowns and averages of star ratings, loads, styles, and ages for
all of our data samples are available from the authors upon request.

Blake and Morey 459

ii) There are more five-star funds than one-star funds and the average star rating
of each sample is above three. This skewness in the ratings of the sample in-
dicates that aggressive growth, equity-income, growth, growth-income, and
small company funds with 10 years or more of retums performed slightly
better than other funds in the Momingstar domestic equity category.^^
iii) The standard deviation of the ratings is about the same in each sample, in-
dicating that the distribution of the ratings does not differ much from one
sample to another.
iv) For the load-funds, most have front-end loads and relatively few have de-
ferred loads.
v) Most of the funds are grouped within the growth and growth-income invest-
ment objectives.
When we examined the average star ratings by style, we found that, for most
of our sample years, aggressive growth and small company funds have fewer
funds and lower averages than the other styles. Moreover, in many samples, the
aggressive growth, equity-income, and small company styles have few, if any,
funds in the lowest or highest star categories. Also, the standard deviations are
about the same in each subsample within each investment style, with the notable
exception being the 1993 aggressive growth subsample.
For the complete fund 1993 sample, as with the seasoned funds sample, the
average star rating is above three and there are relatively more five-star funds than
there are one-star funds. The average rating exceeding three stars is a result of
other investment objectives being grouped into the domestic equity category (see
footnote 22). Other interesting features ofthe complete fund 1993 sample follow.
i) Most of the funds are in the seasoned and middle-aged category; only 14%
of the funds are young funds.
ii) More than one-half of the funds are load funds. Again, loads seem to be an
important factor to consider.
iii) As with the seasoned funds sample, most of the funds are clustered in the
growth and growth-income styles.
iv) Aggressive growth funds fare worse than the other investment objectives in
terms of star ratings.
v) There are no substantial differences in the average star ratings of young,
middle-aged, and seasoned funds, yet the respective distributions are quite
different. In fact, there are no one-star young funds. As stated above, young
funds receive their stars based upon the past three years of retums, so it is
possible that the three years prior to January 1993 did not drive any new
funds into the bottom rating category.

higher average star ratings could be due to seasoned funds performing slightly better, or
it could be a result of other investment objectives (styles), besides those used in this study, being
grouped into the domestic equity category. These other investment objectives include domestic hybrid
funds, convertible bond funds, fund.s termed by Momingstar to be miscellaneous funds, and even
intemational funds up until 1996. Blume (1998) has documented that these other investment objective
funds generally have lower performance and are rated lower than the Aggressive growth, equity-
income, growth, growth-income, and small company funds.


D. Momingstar Scores

Since January 1994, Momingstar has provided the three-, five-, and 10-year
Momingstar retum and risk numbers for all the mutual funds it evaluates. With
that information, plus the subjective weights (20%, 30%, and 50% for the three-,
five-, and 10-year horizons), we can calculate the resultant scores and numeri-
cally rank the funds evaluated here. These scores then allow us to conduct non-
parametric rank correlation tests. (Since the data are not provided before 1994,
we do not conduct these tests either for the seasoned 1992 and 1993 samples, or
for the complete funds 1993 sample.)

E. Alternative Predictors

We compare the Momingstar rankings and scores with those of four alter-
native predictors. Each altemative predictor is calculated during the in-sample
period just prior to fund selection, either during the 10-year period prior to the
out-of-sample evaluation periods when examining the seasoned funds 1992-1997
sample, or during the three-year period prior to the out-of-sample evaluation peri-
ods when examining the complete funds 1993 sample only. For a naive predictor,
we use the fund's average monthly in-sample retum. The second altemative pre-
dictor we use is the in-sample Sharpe ratio.

(2) Sharpe, =

where /?,• - Rp is the mean excess (net ofthe 30-day T-bill rate) monthly retum for
the rth mutual fund during the in-sample period, and cr, is the standard deviation
of the excess monthly retums for the ith mutual fund during the in-sample period.
For two additional altemative predictors, we use Jensen single-index and
four-index alphas, obtained from the following time-series regression model.

(3) Ri, = ai-^

where
Ri, = the excess total retum (net of the 30-day T-bill retum) for fund i in
in-sample month f,
a,- = the alpha for fund i, used as a performance predictor;
Pik = the sensitivity of fund i's excess retum to index k;
Ik, = the retum for index k in in-sample month t; and
£,-, = the random error for fund / in in-sample month t.
For Jensen alphas, K— and h, = the excess total retum ofthe S&P 500 in month
f. For the four-index alphas, K = 4, I, = the excess total retum of the S&P 500
in month t, I2, = the excess total retum of the Lehman Aggregate Bond Index in
month t, h, = the difference in retum between a small-cap and large-cap stock
portfolio based on Prudential Bache indexes in month t, and U, = the difference

Blake and Morey 461

in retum between a growth and value stock portfolio based on Prudential Bache
indexes in month t.^^ We utilize the four-index model because, as Elton, Gruber,
and Blake (1996a) show, this model provides better risk adjustment for mutual
funds than the single-index model.

R Out-of-Sample Evaiuation Periods

When evaluating performance, investors are typically presented with the
one-, three-, five-, and (if available) 10-year past performance windows. Sim-
ilarly, we use one-, three-, and five-year periods to examine the out-of-sample
forecasting ability of Momingstar's ratings. (The 10-year window is outside the
bounds of our sample.) This provides 12 subsamples for performance evaluation
for our seasoned funds 1992-1997 sample and three additional subsamples for
our complete funds 1993 sample.^''

G. Returns Data and Load Adjustments
For the out-of-sample and in-sample retums used in the altemative predic-
tors, the data consist of monthly retums from the Momingstar On-Disk and Prin-
cipia programs. These retums data are adjusted for management, administrative,
12b-l fees, and other costs automatically taken out of fund assets. However, un-
like the Momingstar risk-adjusted ratings, the monthly retum data do not adjust
for sales charges such as front-end and deferred loads.^' Consequently, if we use
the monthly retum data for the out-of-sample retums, the retums on load funds
will be overstated.
Very little attention in the mutual fund performance literature is given to the
treatment of loads in retum data. Although some authors (e.g., Gruber (1996))
have presented results separately for load and no-load funds, most studies (e.g.,
Hendricks, Patel, and Zeckhauser (1993), Elton, Gruber, and Blake (1996a),
Malkiel (1995), and Carhart (1997)) provide no direct adjustment for loads in
their retums data. Loads may be important, especially in this paper since the
Momingstar ratings encompass load-adjusted retums, but the question is how to
deal with them. Should one use front-end loads, deferred loads, or both? When
and for how long should one apply the load? What if the mutual fund has reduced
its load over time (especially the deferred load)? Should one use an average load
adjustment for each month or an annualized load? If one decides to use an annu-
alized load, what interest rate should one use to discount the load factor?
In light of these issues, we adjust the monthly retums of each mutual fund
using an approach similar to Rea and Reid (1998). For both front-end and deferred
loads, we consider an investor who buys and holds the load shares for a fixed
number of months, i.e., 12 months (one year), 36 months (three years), or 60
months (five years). For front-end loads, the investor buying the fund pays the
^'See Elton, Gruber, and Blake (1996a) for a detailed description ofthe Prudential Bache portfolios
used in the four-index model.
^"•A detailed table showing the number of funds, number of merged and liquidated funds, and
number of funds that changed their Momingstar styles for all out-of-sample periods and both sample
groups is available from the authors upon request.
2'Principia Manual (1998), p. 107.


load in a lump sum at the time of purchase. To spread the front-end load across
the period that the shares are held, we use Rea and Reid's assumption that the
investor borrows the amount necessary to pay the load up front and then repays
the loan as an annuity in equal, monthly installments during the holding period.
Hence, the monthly load adjustment reflects the amount that was borrowed and
the interest on the loan. Mathematically, our front-end load adjustment process is

(4) r =
7=1

where r is the monthly interest rate (the monthly geometric average of the one-,
three-, or five-year Treasury yield over the holding period),/ is the front-end load
(expressed as a percent), h is the number of months the fund is held, and/"* is the
monthly front-end load adjustment. Hence, the front-end load adjusted retums
are

(5) Ri, — Kit -J ,

where /?„ is the monthly retum of fund i in month t and Rj^^ is the monthly
front-end-load-adjusted retum of fund i in month f.
As an example of the adjustment, consider a one-year investment in Fi-
delity's Magellan fund starting in January 1992 when Magellan had a front-end
load of 3%, and the one-year Treasury yield was 3.84%, giving a monthly aver-
age rate of 0.31%. Therefore, for the one-year holding (out-of-sample) period,/
= 3%, r = 0.0031, and /i = 12, giving/"" = 0.255%. We then subtract 0.255%
from each of the Magellan fund's 12 monthly retums during 1992 to obtain the
load-adjusted retums.
For the deferred-load adjustment, the process is slightly different in the fact
that the payment of the deferred load does not occur until the end of the hold-
ing period. To convert the deferred load into a monthly payment, the investor is
assumed to have prepaid the load in equal monthly installments. The amount of
the monthly prepayment reflects the deferred load less the interest eamed on the
prepayments. Thus, the equation for the monthly deferred-load adjustment is

(6)

7=1

where d is the deferred load (expressed as a percent) and d"" is the monthly
deferred-load adjustment. Hence, the deferred-load-adjusted retums are

(7) R°^^ = Rit-d"",

where /?,-, is the monthly retum of fund i in month t and R^^'^ is the monthly
deferred-load-adjusted retum of fund i in month t.
As with the front-end loads, we use the monthly geometric average of the
one-, three-, or five-year Treasury yield over the holding period for the interest

Blake and Morey 463

rate. However, in contrast to the front-end load adjustment, we reduce the amount
of the deferred load as the holding period, h, increases. We do this because Mom-
ingstar also reduces the deferred load as the holding period increases. Hence, for
a holding period of 12 months, the full amount of the deferred load is imposed.
For the 36-month holding period, we apply only half of the original deferred load
and in the 60-month holding period, the deferred load completely disappears.

III. Methodology
To measure out-of-sample performance we use four performance metrics. To
examine out-of-sample performance of the Momingstar ratings and the altemative
predictors, we use two methods: dummy variable regression analysis and the non-
parametric Spearman-Rho rank correlation test.

A. Out-of-Sample Performance Measurement

We use four performance metrics from the existing performance literature to
measure out-of-sample performance: the Sharpe (1966) ratio, the mean monthly
excess retum, a modified version of Jensen's (1968) alpha, and a modified version
of a four-index alpha (Elton, Gmber, and Blake (1996a)). For each performance
metric, we examine both non-load-adjusted and load-adjusted versions. We report
results only for the load-adjusted Sharpe ratio, the load-adjusted mean monthly
excess retum, the non-load-adjusted modified Jensen alpha, and the non-load-
adjusted modified four-index alpha.^*
The load-adjusted Sharpe ratio for fund i is

(8) Sharpe, =

where Rf^ — RF is the mean excess (net of the 30-day T-bill rate) load-adjusted
monthly retum for the z'th mutual fund during the evaluation (out-of-sample) pe-
riod andCT/is the standard deviation of the excess load-adjusted monthly retums
for the Ith mutual fund during the evaluation period. The non-load-adjusted
Sharpe ratio is essentially the same as equation (2), except that it uses the out-
of-sample period. The load-adjusted mean monthly excess retum is R]-^ - Rf.
The non-load-adjusted mean monthly excess retum is Ri - RF.
The non-load-adjusted modified Jensen and four-index alphas are calculated
using a methodology similar to Elton, Gruber, and Blake (1996a). Specifically, for
each seasoned funds subsample, we utilize a time-series period of monthly non-
load-adjusted retums going back 10 years from the selection date and forward to
the end of the out-of-sample evaluation period to obtain an estimate of the inter-
cept from either the single-index or four-index model regression (equation (3)).
For our complete funds 1993 sample group, we utilize a time-series period of
^^The results for the metrics that are not reported in this paper, i.e., those for the non-toad-adjusted
Sharpe ratio, the non-load-adjusted mean monthly excess retum, the load-adjusted modified Jensen
alpha and the load-adjusted modified four-index alpha, are essentially the same as their load/non-load
counterparts. These results are available from the authors upon request.


monthly non-load-adjusted retums going back three years from the selection date
and forward to the end of the out-of-sample evaluation period to obtain an esti-
mate of the intercept from either the single-index or four-index model regression
(equation (3)).
To obtain the alphas, we add the average monthly residual during the evalu-
ation period to the intercept. For example, to obtain a modified Jensen alpha for
a seasoned fund's one-year out-of-sample performance measure in the 1992 sub-
sample, we mn the one-index model on monthly retums starting in January 1982
and ending in December 1992 (11 years) to obtain an estimate of the intercept.
We then add the average of the fund's residuals during the one year after the se-
lection date (the evaluation period) to the estimated intercept to obtain the fund's
modified Jensen alpha.
To obtain alphas for funds that merged or liquidated during the evaluation
period, we first run two regressions: i) a regression using the fund's retums going
back either 10 or three years from the selection date and ending in the month prior
to the fund's disappearance, and ii) a regression run over the entire regression pe-
riod using the retums on an equally-weighted portfolio formed each month from
the existing funds in the sample. We then form a weighted average of: i) the fund's
estimated intercept plus the fund's average residual during the time it survived in
the evaluation period, and ii) the estimated intercept plus the average residual dur-
ing the remaining time in the evaluation period of the equally-weighted portfolio,
where the fund's weight is the fraction of the evaluation period it survived and
the equally-weighted portfolio's weight is the remaining fraction. This provides a
performance measure for an investor who buys a remaining fund in the sample at
random if the original fund merges or liquidates.
For the load-adjusted modified Jensen and four-index alphas, we do not use
load-adjusted retums, since we use both out-of-sample and in-sample data for
these measures. We could apply loads to the in-sample data, however, doing so
would raise a number of problems. First, the loads during the in-sample period
may be quite different from those in the out-of-sample period. Second, and more
importantly, it is not clear how we should deal with loads before an investor owns
a fund. Again, our assumption in this paper is that the investor selects the funds
at the time they are rated by Momingstar. Moreover, our load adjustment depends
upon how long the investor holds the fund. If we were to assume that the investor
already owned the fund before the out-of-sample period started, and paid loads
during the in-sample period, it would be difficult to determine the correct load to
assess for the out-of-sample period.
As an altemative, we adjust the single-index and four-index alphas for loads
by using an added (0,1) dummy variable in equation (9), where 1 = load funds
and 0 = no-load funds.

B. Dummy Variable Regression Anaiysis

The first method we use to examine out-of-sample predictive performance is
a cross-sectional dummy variable regression analysis that allows us to examine
the Momingstar star ranking group differences in performance predictability. To
make the results for the altemative predictors comparable to those for the Mom-

Blake and Morey 465

ingstar star groups, we divide the funds into five subgroups after ranking them
in descending order by each of their altemative predictors. These five altema-
tive predictor subgroups are not quintiles, since we wanted to preserve the same
number of funds in each altemative predictor subgroup as we have in each of
the five Momingstar star groups. As an example, consider our January 1992 sea-
soned fund subsamples. The same 263 funds are in each of these subsamples: 18
five-star funds, 93 four-star funds. 111 three-star funds, 33 two-star funds, and 8
one-star funds. Therefore, for our 1992 seasoned fund subsamples, for any one
of our altemative predictors, group five has 18 funds with the highest altemative
predictor, group four has the next highest 93 funds, etc.
We estimate the following equation for each of the 12 subsamples for the
seasoned funds and for each of the three subsamples for the 1993 complete set,

(9) Si = jo-i-'yiD4i + j2D3i + j3D2i + j4Dli + Ui,

where:
5, = out-of-sample performance metric for fund ;, i.e., the load-adjusted
Sharpe ratio, the load-adjusted mean monthly retum, the non-load ad-
justed single index alpha, the non-load adjusted four-index alpha;
D4 — 1 if a four-star fund or if in altemative predictor group four, 0 if not;
Z)3 = 1 if a three-star fund or if in altemative predictor group three, 0 if not;
D2 = 1 if a two-star fund or if in altemative predictor group two, 0 if not;
Dl = 1 if a one-star fund or if in altemative predictor group one, 0 if not;
J = 1 through A^, where N is the total number of funds in the subsample.
In equation (9), the five-star fund group or the altemative predictor group
five is the reference group for the dummy variable regression.^^ Hence, when
using the load-adjusted Sharpe ratio as the out-of-sample performance measure,
the coefficient 70 represents the expected load-adjusted Sharpe ratio when all the
dummy variables are equal to 0, and coefficients 71 through 74 represent the dif-
ferences between the dummy variables and the reference group. The f-statistics
on the coefficients provide a test of the significance of the difference between an
individual dummy group and the reference group.
We use the five-star funds or altemative predictor group five as a reference
group because they provide a ceiling from which we can compare the performance
of the lower group funds. If the star ratings or altemative predictors accurately
forecast out-of-sample performance, we should see increasingly negative (and
significant) coefficients as we move from 71 to 74.

C. Spearman-Rho Rani< Correlation Test

As a final test, we use the two-tailed Spearman-Rho rank correlation test to
examine the rank correlations of both the Momingstar scores and the altemative

^'We also performed all of the dummy variable regressions using the three-star funds or the alter-
native predictor group 3 as the reference group. The results, which did not change when using this
reference group, are available from the authors.


predictors with the out-of-sample performance measures. Since Momingstar pro-
vides the data to rank the funds beginning in 1994, we only examine this test for
samples that begin in 1994 or later. The Spearman-Rho has a null hypothesis of
no correlation between the two rankings and is a non-parametric test.
For this test, we follow the methodology of Elton, Gruber, and Blake (1996a).
For each fund in the sample, we examine the four different out-of-sample mea-
sures: the (load-adjusted) Sharpe ratios, the (load-adjusted) mean monthly excess
retums, the Jensen alphas, and the four-index alphas. We first sort all the funds
in descending order by either their in-sample Momingstar scores or, in the case
of the altemative predictors, by their in-sample predictor's performance. Next,
we organize the data into deciles and compute the average for each decile. Our
goal is then to examine whether the decile ranking given by either the Mom-
ingstar scores or by the altemative predictors corresponds to the decile rankings
of the four out-of-sample performance measures. If the Momingstar system or
the altemative predictors forecast well out-of-sample, then there should be close
correlation between the in-sample and the out-of-sample rankings.

IV. Momingstar Rating Results
We present the predictive ability of the Momingstar ratings in two broad sec-
tions. First, we report the results using the 1992-1997 seasoned funds subsam-
ples. In Section IV.A, we discuss the dummy variable results for the overall sam-
ples, the dummy variable results for the samples organized by style groups, the
Spearman-Rho rank correlation results for the overall samples, and the Spearman-
Rho rank correlation test for the samples organized by style groups. In Section
IV.B, we report the results of the complete funds 1993 sample. All the regres-
sions in Section IV were tested for heteroskedasticity using the White (1980) test.
None of the regression residuals exhibited evidence of heteroskedasticity at the
10% level.

A. 1992-1997 Seasoned Funds Sample Results
In this subsection, we discuss the dummy variable regression results on our
seasoned funds groups, both for the overall samples and for the samples broken
further down into style subgroups. For the overall samples, we do not examine
the mean monthly retum results, since funds with different styles will likely have
different mean monthly retums.

1. Dummy Variable Regression Analysis on Overall Seasoned Fund Samples
The Load-Adjusted Sharpe Ratio. Table 1 presents the dummy variable regres-
sion analysis in which we examine how well the Momingstar stars predict out-
of-sample fund performance, as measured by the load-adjusted Sharpe ratio, for
the overall seasoned fund samples. First, the Sharpe ratio results in Table 1 show
that the 70 coefficients, the constants in the dummy variable regressions, differ
from sample to sample. The 1992 constant is close to zero and insignificant, the
1994 constant is well below zero and significant, and the 1993 and 1995-1997
constants are all positive and significant. These results indicate that the reference

Blake and Morey 467

group (the five-star funds) performs quite differetitly in different years. The up-
and-down performance of the five-star Sharpe ratios is consistent with the perfor-
mance of the S&P 500 index's mean monthly excess retums. Second, the results
show that the four- and three-star funds do not diverge from the five-star funds
in terms of out-of-sample performance. Only three of the 24 coefficients (71 and
72 for the 12 samples) are significant, indicating no significant difference in out-
of-sample performance of median- and top-rated funds. In many cases, even the
signs on the coefficients are the opposite of what one would expect. Third, there
is some evidence that the Momingstar ratings predict the low-performing funds.
The 73 and 74 coefficients are generally negative and significant (12 of the 24 73
and 74 coefficients), indicating that the performance of one- and two-star funds is
significantly worse than the five-star funds. Fourth, the R^ and F-statistic values
for the samples differ dramatically—the 1992 one-year sample has an R^ of 0.02
while the 1997 one-year sample has an/?^ of 0.17.

TABLE 1
Dummy Variable Regressions Using Momingstar Stars

Sample To (constant) Tl (<.star) T2 (3-star) T3 (2-sta/) ~l« ('-star) F-Stal.

1992 0.06 0.11 0.07 0.06 0.01 0.02 1.13
(1.09) (1.76) (1.12) (0.78) (0.10)
1993 0.26* -0.03 -0.05 -0.11 -0.21 0.02 1.27
(4.13) (0.42) (0.78) (1.51) (1.40)
1994 .-0.18* -0.05 -0.05 -0.09 -0.32* 0.05 4.11*
(3.92) (1.02) (1.02) (1.69) (3.74)
1995 0.69- 0.14* 0.10 -0.03 -0.42* 0.11 10.31*
(9.29) (1.77) (1.37) (0.35) (3.51)
1996 0.25* 0.05 0.02 -0.03 -0.15* 0.06 5.82*
(8.19) (1.51) (0.55) (0.92) (2.67)
1997 0.39- -0.03 -0.07* -0.15* -0.35* 0.17 20.28*
(12.69) (0.91) (2.23) (4.36) (6.72)
Three-Year
1992 0.01 0.05 0.04 0.06 0.01 0.02 1.01
(0.07) (1.50) (1.36) (1.84) (0.21)
1993 0.28* -0.03 -0.06 -0.14* -0.12 0.08 5.51*
(8.99) (1.02) (1.70) (3.66) (1.57)
1994 0.26" -0.01 -0.02 -0.08* -032* 0.15 12.88*
(8.87) (0.01) (0.61) (2.23) (5.68)
1995 0.42* 0.03 0.01 -0.04 -0.26* 0.12 11.66*
(12.61) (0.74) (0.34) (1.10) (4.79)
Five-year
1992 0.24* 0.01 -0.01 -0.01 -0.11* 0.03 2.30
(9.91) (0.18) (0.32) (0.31) (2.50)
1993 0.34* -0.04 -0.06* -0.13* -0.08 0.08 6.09*
(12.65) (1.31) (2.06) (4.03) (1.25)
Sample: Funds wiin 10 years or more of in-sample returns (Seasoned Funds 1992-1997 Sample Group).
Out-ol-Sample Perlormar^ce Measure: Load-Adjusted Sharpe Ratio,
(-statistics are in parentheses.
*indicates significance at the 5% level.

The Modified Jensen Alpha and Four-Index Alpha. Results for the modified
Jensen and four-index alphas continue to demonstrate the same pattems as the
Sharpe ratio: little, if any, significant difference between the five-, four-, and
three-star rated funds (with the 1993five-yearsample providing the only evidence
of significance in the right direction), some evidence of negative and significant


differences between the low-rated and the five-star funds, and wide swings in the
constant and R^ values. In addition, the one- and four-index models show that, in
most cases, the five-star funds have negative (and sometimes significant) alphas
(the 70 coefficient).^^

2. Dummy Variable Regression Analysis on Samples Organized by Style
Groups
We also examined the ability of the Momingstar stars to predict out-of-
sample performance when the samples are broken into the five style groups. The
results are very similar across out-of-sample performance measures and also to
the dummy variable analysis on the unbroken sample.^'
First, there is very little ability to predict significant negative differences
between the five-, four-, and three-star funds. In fact, for the out-of-sample load-
adjusted mean monthly retum, 33 of the 60 coefficients for 72 (the three-star fund)
are positive. Second, the growth and growth-income styles ratings show some
ability to predict low-performing funds. However, this result does not extend to
the other styles: the low ratings of aggressive growth, equity-income, and small
company funds show relatively little ability to detect significant differences in
out-of-sample performance. Third, there are vast differences in the constant term
across styles and samples. For example, using the 1994 one-year sample, the
small company five-star funds post a solid gain, while every other style shows a
negative value for the constant.
The results for the aggressive growth and equity income styles, and to a
lesser extent for small company funds, should be interpreted carefully since there
are relatively few of these funds in the seasoned funds samples. In fact, for a
number of samples, the equity-income style does not have a single one-star fund.
The small sample may be the reason that the stars for growth and growth-income
funds predict low future performance better than the other styles.

3. Spearman-Rho Rank Correlation Tests for Overall Seasoned Funds Samples
Table 2 displays the Spearman-Rho rank correlation test results for the over-
all seasoned fund samples. For each of the six samples in which we have avail-
able Momingstar scores. Table 2 shows the Spearman-Rho rank correlations of
the in-sample Momingstar scores with the out-of-sample decile averages of the
performance measures across all 10 deciles, and across both the top five deciles
and the bottom five deciles. The results show the same basic pattem found in
the dummy regression analysis on the overall sample: the low scores predict poor
future performance and the high scores have, at best, only mixed ability to pre-
dict future performance. In examining the rank correlation coefficients on all 10
deciles, several performance measures are relatively well correlated with the in-
sample Momingstar scores. In fact, in four of the six samples for the Sharpe
^^Tables on the re.sulLs for the modified Jen.sen and four-index alpha.s are available from the authors.
AI.SO, .since the samples are not divided by investment objective, we do not report the load-adjusted
mean monthly retum results in this section. Funds with different styles will likely have different mean
monthly returns. In the sections in which we organize .samples into their respective style groups, we
use the load-adjusted mean monthly retum as one of the out-of-sample measures.
^'Detailed tables of the resulLs for the out-of-sample performance measures when the samples are
broken into style groups are available upon request.

Blake and Morey 469

ratio, one of the six samples for the Jensen single-index alpha, and two of the
six samples for the four-index alpha, we cannot reject the null hypothesis of no
correlation in the rankings at the 95% confidence level. However, examining the
correlation coefficients of the top five and bottom five deciles, we see that overall
rank correlation results are largely based on the ability ofthe low scores to predict
poor future performance. In most cases, the correlation coefficients for the bottom
five decile are much larger than those for the top five decile. Generally, the rank
correlation coefficients for the top five deciles are actually negative, indicating
that high scores do not accurately predict superior future performance.

TABLE 2
Spearman-Rho Rank Correlations Using Decile Averages of In-Sample Momingstar Scores
with Decile Averages of Out-of-Sample Performance Measures

Rank Correlation

Load-Adjusted Non-Load-Adjusted Non-Load-Adjusted
Sample Deciles Sharpe Ratio Jensen Alpha 4-lndex Alpha

One-Year
1994 All 0.806* 0.600 0.806*
Top 5 -0.200 -0.700 -0.400
Bottom 5 0.600 0.600 0.800
1995 All 0.430 0.467 0.648*
Top 5 -0.700 -0.500 0.600
Bottom 5 1.000* 0.700 0.300
1996 All 0.758* 0.370 0.079
Top 5 -0.400 -0.900* -0.900*
Bottom 5 0.900* 0.700 0.300
1997 All 0.927* 0.648* -0.297
Top 5 0.500 -0.700 -0.900*
Bottom 5 0.900* 1.000* 0.700
Three-Year
1994 All 0.685* 0.673 0.442
Top 5 -0.100 -0.200 -0.700
Bottom 5 0.700 0.700 0.600
1995 All 0.491 0.382 0.261
Top 5 -1.000* -0.600 -0.100
Bottom 5 1.000* 1.000* 0.400
All funds have 10 or more years of In-sample returns (seasoned funds 1992-1997 group).

4. Spearman-Rho Rank Correlation Tests for Samples Organized by Style
Groups
We also examined the results of the Spearman-Rho rank correlation tests
for the samples when broken into their respective style groups.-'" As with the
dummy variable results for the samples broken into style groups, we examined
the out-of-sample load-adjusted mean monthly retum, the load-adjusted Sharpe
ratio, the non-load-adjusted single-index alpha, and the non-load-adjusted four-
index alpha. The results mirror the dummy variable results when the samples
are organized by style. For the aggressive growth, equity-income, and, to a lesser
extent, the small company sample groups, we do not see much positive correlation
between the Momingstar scores and the out-of-sample metrics. This is true for the
rank tests ofthe overall 10 deciles, the topfivedeciles, and the bottom five deciles.

^"A detailed table containing the resulLs of that examination is available from the authors.


Again, the reason for this may be that the sample sizes are not large.-" However,
with the growth and growth-income samples, we see the pattem suggested by the
overall Spearman-rho rank correlations tests in Table 2: low correlations using
the top five deciles and higher correlations using the bottom five deciles. In fact,
in the growth fund sample, every bottom five decile rank correlation is higher in
value than the top five decile rank correlation. The results again suggest that the
Momingstar scores are weak in terms of predicting high future performance and
yet have some ability to predict underperforming funds.

B. Complete Funds 1993 Sample Results

1. Dummy Variable Regression Analysis on the Overall Complete Funds 1993
Samples
Table 3 presents the results from the dummy variable regressions for our
overall complete funds 1993 sample and shows the same pattems detected in the
seasoned funds overall sample results. First, there is a relatively strong ability to
predict low-performing funds, especially in the longer out-of-sample terms. Of
the 18 coefficients for 73 (two-star) and 74 (one-star), 15 are negative and sig-
nificant, indicating that low-rated funds do perform significantly worse in terms
of risk-adjusted performance. Second, there is only weak ability to predict high-
performing funds. Only two of the nine coefficients for 72 (three-star) and zero
of the nine coefficients for 71 (four-star) are negative and significant. In fact,
only five of the nine 71 (four-star) coefficients have the "correct" negative sign.
Third, the Momingstar stars do a slightly better job of predicting out-of-sample
performance when using the four-index alpha.

2. Dummy Variable Regressions for Samples Organized by Age
Panels A-C of Table 4 present the results for the dummy variable regressions
in which we use samples organized by age. Table 4, Panel A reports the results
for young funds (three to less than five years of in-sample retums); Panel B re-
ports the middle-aged funds (five to less than 10 years of in-sample retums); Panel
C reports the seasoned funds (10 or more years of in-sample retums). There is
evidence of an ability to predict poor future performance, especially among sea-
soned and middle-aged funds. Using the four-index alpha for the middle-aged
funds shows that the Momingstar stars have a strong ability to predict weak per-
formance, as most of the coefficients for the lower rated funds are negative and
strongly significant. Among the young funds, we do not see much evidence of
ability to predict weak performance, but this is probably because there are no
one-star funds in the young funds sample group and, in general, relatively few
young funds in the sample.
In predicting high-performing funds, the Momingstar stars are, at best, mildly
successful. In the middle-aged and seasoned fund subsamples, only five of the 18
coefficients for 72 (three-star) are significant and negative, yet two of the 18 are

''For the equity-income sample, we did not perform the test over many samples since there were
not enough observations to create the deciles. We required that there be at least 20 ob.servations .so
that each decile would have at least two observations.

Blake and Morey 471

TABLE 3
Dummy Variable 1
Regressions Using Momingstar Stars

Sample To (constant) T1 (4-star) 72 (3-star) T3 (2-star) tA (1-slar) F-Stat.

Oul-oi-Sample Perlormahce Measure: Load-Adjusted Sharpe Ratio
1 year 0.25* 0.01 -0.03 -0.08 -0.07 0.01 2.14
(6.98) (0.36) (0.66) (172) (0.70)
3 year 0.26* -0.01 -0.02 -0.08* -0.13* 0.05 7.90*
(16.48) (0.25) (1.14) (3.79) (2.72)
5 year 0.28* 0.02 0.01 -0.05* -0.08 0.08 6.00*
(20.86) (1.10) (0.49) (2.66) (1.94)
Out-ol-Sample Perlormahce Measure: Non-Load-Adjusted Jehsen Ihdex Alpha

1 year 0.26* -0.01 -0.05 -0.10 0.01 0.01 0.59
(3.37) (0.01) (0.57) (1.04) (0.01)
3 year -0.10* 0.02 -0.04 -0.16* -0.34* 0.04 6.72*
(2.30) (0.35) (0.76) (2.97) (2.67)
5 year -0.21* 0.07 0.03 -0.10* -0.26* 0.04 7.31*
(5.45) (1.70) (0.65) (1.94) (2.29)
Out-ol-Sample Perlormahce Measure: Noh-Load-Adjusted 4-lhdex Alpha
1 year 0.13* -0.02 -0.04 -0.20* -0.58* 0.07 4.47*
(2.00) (0.23) (0.59) (2.36) (2.96)
3 year 0.05 -0.06 -0.10* -0.20* -0.41* 0.04 6.20*
(1.18) (1.20) (2.22) (3.67) (3.26)
5 year 0.06" -0.04 -0.09* -0.15* -0.31* 0.04 7.05*
(1.99) (1.25) (2.64) (3.88) (3.35)
Sample: All funds from the complete funds 1993 sample group (635 funds),
/-statistics are In parentheses.
*indicates significance at tfie 5% level.

significant and positive, indicating that the three-star funds perform better out-of-
sample than the five-star funds. Among the young funds, many of the coefficients
have the predicted negative signs, yet there is little ability to detect significantly
different performance between median and high-rated funds.

3. Dummy Variable Regressions for Samples Organized by Style

We also examined the results from the dummy variable regressions when the
samples are organized by style.^^ The results for low-performing funds are very
similar to the seasoned fund samples' results when the samples are organized by
style. Only in the growth and growth-income styles is there a relatively strong
ability to predict low performance, as many of the coefficients for 73 (two-star)
and 74 (one-star) are negative and significant, particularly in the longer out-of-
sample periods.
In predicting high-performing funds, the small company, aggressive growth,
and equity income funds do not demonstrate much ability. However, for the
growth and growth-income funds, there is evidence of ability to predict winning
funds. For both the growth and growth-income subsamples, almost all coefficients
show the postulated negative sign, and many of the coefficients are significant for
the growth-income subsample. Of course, the success of these subsamples may
be largely related to the sample period. In our earlier analysis of the seasoned
funds samples, the 1993 subsamples provide some ofthe strongest support (albeit

'^A detailed table of those resulLs is available from the authors.

472 Journal of Financial an(d Quantitative Analysis

TABLE 4
Dummy Variable Regressions Using Momingstar Stars Organized by Age

Sample/Age To (constant) Vi (4-star) Tg (3-star) TS (2-star) 'l'4(1-star) R2 F-Stat.

Pane/ A. Youhg Funds (less thah 5 years ol irt-sampte returns)
Out-ol-Sample Performance Measure. Load-Adjusted Sharpe Ratio
•
One-year 0.33- -0.05 -0.04 -0.25* NA 0.07 2.06
(3.33) (0.44) (0.40) (1.99)
Three-year 0.28* -0.08 0.01 -0.06 NA 0.06 1.97
(7.27) (1.31) (0.02) (1.31)
Five-year 0.29* -0.01 0.03 -001 NA 0.03 0.89
(8.39) (0.15) (0.88) (0.06)
Out-of-Sample Performance Measure: Non-Load-Adjusted Jensen Alpha
One-year 0.42- -0.16 -0.18 -0.31 NA 0.02 0.44
(1.99) (0.61) (0.75) (1.14)
Three-year -0.04 -0.10 -0.01 -0.07 NA 0.02 0.72
(0.46) (0.92) (0.01) (0.63)
Five-year -0.15 -0.01 0.07 0.07 NA 0.02 0.50
(1.79) (0.03) (0.74) (0.64)
Out-of-Sample Performance fi^easure. Noh-Load-Adjusted 4-lnciex Alpha
:
One-year 0.24 -0.13 -0.11 -0.35 NA 0.04 1.10
(1.43) (0.64) (0.60) (1.63)
Three-year 0.09 -0.14 -0.11 -0.17 NA 0.03 0.83
(0.99) (1.30) (1.12) (1.52)
Five-year 0.12 -0.09 -0.14 -0.13 NA 0.04 1.34
(1.75) (1.10) (1.89) (1.56)
Panel B. Middle-Aged Funds (greater than 5 and less than 10 years ol in-sample returns)
Out-of-Sample Performance Measure. Load-Adjusted Sharpe Ratio
.
•
One-year 0.21' 0.08 0.01 0.01 0.05 0.02 1.24
(4.45) (1.42) (0.05) (0.22) (0.33)
Three-year 0.24- 0.03 -0.01 -0.04 -0.14* 0.07 5.09*
(12.38) (1.44) (0.01) (1.46) (2.49)
Five-year 0.25* 0.05* 0.04* -0.01 -0.09 0.08 6.25*
(15.14) (2.74) (2.06) (0.52) (1.77)
Out-of-Sample Performanoe Measure. Non-Load-Adjusted Jensen Alpha
:
One-year 0.23* 0.C7 -0.03 -0.09 0.10 0.01 0.69
(2.23) (0.57) (0.23) (0.66) (0.34)
Three-year -0.13* 0.07 -0.01 -0.11 -0.40* 006 4.42*
(2.39) (1.08) (0.24) (1.53) (2.53)
Five-year -0.30* 0.17* 0.13* -0.04 -0.27 009 7.03*
(5.73) (2.86) (2.25) (0.62) (1.79)
Out'Of-Sampfe Performance Measure: Non-Load-Adjusled 4-lndex Alpha
One-year 0.13 0.02 -0.08 -0.24 -0.49 0.04 2.53*
(1.36) (0.15) (0.73) (1.90) (1.82)
Three-year 0.08 -0.04 -0.14* -0.20* -0.47* 0.07 4.86*
(1.42) (0.65) (2.25) (2.77) (2.98)
Five-year 0.07 -0.01 -0.10* -0.16* -0.35* 0.09 7.11*
(177) (0.12) (2.37) (3.10) (3.08)
(continued on next page)

not that strong) for Momingstar stars predicting high-performing funds, but it is
questionable whether these results would carry over to other sample periods.^^

C. Load/No Load Counterparts for Out-of-Sample Data

All results in Section IV were calculated using the load/no-load counterparts
of the out-of-sample performance measures, i.e., non-load-adjusted Sharpe ra-
tios, non-load-adjusted mean monthly excess retums, and load-adjusted (using a

1993 seasoned fund sample.? show more predictability than most other samples whether
using the Momingstar stars or the altemative predictors (see Section V).

Blake an(d Morey 473

TABLE 4 (continued)
Dummy Variabie Regressions Using Momingstar Stars Organized by Age

Sample/Age To (constant) T1 (4-star) T2 (3-star) T3 (2-star) T4(i-star) FT F-Stat.

Panel C. Seasoned Funds (10 years or more of in-sample returns)
Out-of-Sample Performance Measure: Load-Adjusted Sharpe Ratio
One-year 0.26* -0.03 -0.05 -0.11 -0.19 0.02 1.15
(4 13) (0.42) (0.78) (1.48) (1.28)
Three-year 028* -0.03 -006 -0.14* -0.11 0.07 5.28*
(8.97) (1.02) (1.69) (3.62) (1.38)
Five-year 0.34* -0.04 -0.06* -0.13* -0.08 0.08 6.00*
(12.61) (1.30) (2.04) (4.01) (1.22)
Out-of-Sample Performance Measure: Non-Load-Adjusted Jensen Index Alpha
One-year 0.20 0.01 0.01 -0.01 -0.04 0.01 0.01
(1.45) (0.10) (0.04) (0.01) (0.12)
Three-year -0.08 -0.01 -0.09 -0.28* -0.25 0.06 4.18*
(0.86) (0.09) (0.96) (2.61) (1.13)
Five-year -0.09 -0.06 -0.15 -0.29* -0.27 0.07 5.03*
(1.18) (0.71) (1.85) (3.22) (1.46)
Out-of-Sample Performance Measure: Non-Load-Adjusted 4-lndex Alpha
One-year 0.07 0.02 0.05 -0.06 -0.62* 0.03 1.88
(0.58) (0.16) (0.37) (0.44) (2.17)
Three-year -0.03 -0.01 -0.03 -0.18 -0.28 0.03 2.08
(0.32) (0.11) (0.29) (1.71) (1.32)
Five-year 0.01 -0.04 -0.03 -0.14 -0.21 0.03 1.70
(0.13) (0.55) (0.46) (1.77) (1.35)
All from complete funds 1993 sample group,
t-statistics are in parentheses.

dummy variable for loads in equation (9)) modified Jensen and four-index alphas.
The results were generally the same as those reported above and are available
upon request.

V. Alternative Predictor Resuits
The results so far indicate that Momingstar ratings do not generally pre-
dict superior fund performance but do have some predictive power for poor-
performing funds. Can an investor do as well by choosing funds based on al-
temative predictors?-''' To answer this question, we examine a naive predictor that
uses in-sample mean monthly retums, an in-sample Sharpe ratio, an in-sample
single-index alpha, and an in-sample four-index alpha.
As with the Momingstar star and score tests, we use the altemative predictors
on both the seasoned funds 1992-1997 sample group and the complete funds 1993
sample group. Hence, again we have two different sets of results. Since presenting
all of the results for each altemative predictor would result in an unwieldy number
of additional tables, we summarize our results in Tables 5-8 for the seasoned
funds 1992-1997 sample and Tables 9-12 for the complete funds 1993 sample.
All the regression results reported in Section V were tested for heteroskedasticity
using the White (1980) test. None of the regression residuals exhibited evidence
of heteroskedasticity at the 10% ^^
^We thank Stephen Brown for suggesting an examination of that question.
•"Section V's results are primarily for the overall samples. Except in Table 12, we do not report
the results for the sample.s that are organized by .style or age. The altemative predictor re.sulLs for the


A. 1992-1997 Seasoned Funds Sample Results

1. Dummy Variable Regression Results
We rank the funds based on the in-sample altemative predictor and then put
them into five groups that match the number of funds in each of the five Mom-
ingstar star groups. This way we can construct the same dummy variable regres-
sion analysis we used for the Momingstar stars.
Although there are nominally four altemative predictors, we actually have
five since we use two variants for the naive predictor. The first variant allocates
the rankings on the basis ofthe in-sample mean monthly retums: if there were 15
five-star funds and 25 four-star funds, the highest 15 funds according to their in-
sample mean monthly retum would receive five's and the next 25 would receive
four's. In the second variant, we first examine how many Momingstar stars are
given within each style group and then rank order the funds by their in-sample
mean monthly retum within their various style groups. For example, in the 1992
sample there are 24 aggressive growth funds of which zero funds received five
stars, five funds received four stars, eight funds received three stars, six funds
received two stars, and five funds received one star. Hence, we would rank the
24 aggressive growth funds by their in-sample mean monthly retum and then give
the top five aggressive growth funds four's, the next eight aggressive growth funds
three's, etc. This way we use mean monthly retums as an altemative predictor and
yet can still be sensitive to style differences.
For each of the five altemative predictors, equation (9) is then estimated for
the 12 samples and for the three different out-of-sample performance measures.
Hence, for each altemative predictor we calculate results that are similar in form
to those in Table 1.^^
Table 5 summarizes the significance level results. The left-hand column re-
ports the number of times out of 144 coefficients (four coefficients, 12 samples,
and three out-of-sample performance measures) that the predictor produces a sig-
nificantly negative coefficient for 71, 72, 73, or 74. The next column reports the
number of times that the predictor produces a significantly positive coefficient for
7i. 72. 73, or 74. Hence, high numbers in the first column indicate a considerable
amount of predictive ability for the predictor and high numbers in the second col-
umn indicate that the predictor is not very successful. The other columns indicate
which of the coefficients, 71, 72,73, or 74, are significantly negative.
Table 5's results show several interesting findings. First, the Momingstar
stars are in the middle in terms of predicting future performance. The naive pre-
dictor that uses the styles and the single-index alpha predictor have very similar
predictive performance to the Momingstar stars. The naive predictor, in which
no adjustment is made for styles, and the four-index alpha generally do worse,
and the Sharpe ratio does considerably better than the Momingstar stars. Second,
for every predictor, including the Momingstar stars, the ability to predict high-
performing funds is quite weak, yet the ability to predict low-performing funds
is quite high. This result is consistent with those found in some other studies

samples organized by style and age were generally similar to those presented in Section IV. They are
available from the authors.
''We summarize the.se results in tables available from the authors.

Blake ancj Morey 475

TABLE 5
Summary of the Ability of the Alternative Predictor Stars and Momingstar Stars to Forecast
Out-of-Sample Performance—Significance Levels

# of times U of times
(out of 144) (out of 144)
the predictor the predictor * cf cases # of cases # of cases # of cases
produces a produces a (out of 36) (out of 36) (out of 36) (out of 36)
significantly* significantly* the the the the
negative positive coefficient. coefficient. coefficient. coefficient.
coefficient coefficient T1 (4-star) • is T2 (3-star). is T3 (2-star). is T4(1-star).iS
for 7, . 7 2 . for 7 , . 72. significant significant significant significant
Predictor T3.Or74 T3. ° ' T4 and negative and negative and negative and negative

10-year 35 14 1 2 8 24
mean
returns;
stars
allocated
by style
10-year 39 38 5 5 7 22
mean
returns; no
adjustment
for styles
10-year 49 1 3 8 9 29
Sharpe
ratio
10-year 36 7 0 3 6 27
single-
index
alpha
10-year 27 48 1 2 5 19
4-index
alpha
fvlorningstar 39 10 0 4 13 22
Sampfes Examined: The 12 Seasoned Fund Samples (not broken up into style categories), i.e.. 1992 (1-. 3-. and 5-year).
1993 (1-. 3-. and 5-year). 1994 (1- and 3-year). 1995 (1- and 3-year). 1996 (1-year), and 1997 (1-year).
Out-of-Sample Metrics Examined: Load-Adjusted Sharpe Ratio. Non-Load Adjusted Single-Index Alpha. Non-Load Ad-
justed 4-lndex Alpha.
Test Examined: The alternative predictor is used to allocate the stars. The frequency of the stars is the same as with
the Morhingstar stars except that they are allocated on the basis of the alternative predictor rather than the Momingstar
method. With these stars, we then examine equation (9). Hence, there are 36 equations estimated (12 samples. 3 out-
of-sample performance metrics) of which each equation has 4 coefficients. 7 i . 72. T3. T4. (hot including the constant).
Hence, there are 144 coefficients examined for each alternative predictor.
*Significant at the 10% level.

on performance predictability (see, e.g., Carhart (1997)) that show it is generally
possible to predict losers (but not winners) in terms of mutual fund performance.
Tables 6 and 7 complement Table 5. Table 6 provides information on where
the negative and significant cases are located with respect to the out-of-sample
measures. In general, the results are spread out relatively evenly among the three
out-of-sample performance measures.
Table 7 examines the relative coefficient signs instead of the significance
levels, and specifically reports the number of times that the coefficient sign for
highly rated funds is greater than that for funds that are two levels worse in terms
of ratings. That is, it examines the number of cases (out of a total of 36) where
70 (5-star) > 72 (3-star)i7l (4-star) > 73 (2-star). Or 72 (3-star) > 74 (I-star)-
The results in Tables 6 and 7 are similar to those presented in the rest of
the paper. First, on the basis of these coefficient signs, the Momingstar stars do
not illustrate significantly better predictive ability than the other predictors. The

476 Journal of Financial anti Quantitative Analysis

TABLE 6
Out-of-Sample Performance—Significance Levels Organized by
Out-of-Sample Performance Measure

# of times out of 48 (12
# of times out of 48(12 samples; 4 coefficients) # of times out of 48 (12
samples; 4 coefficients) the predictor produces a samples; 4 coefficients)
the predictor produces a significantly* negative the predictor produces a
significantly* negative coefficient for 71. 72. 73. significantly* negative
coefficient for 7 i . 72.73. or 74. using the coefficient for 71. 72.73.
or 74. using the non-ioad-adjusted or 74. using the
load-adjusted Sharpe ratio single-index alpha as the non-load-adjusted 4-index
as the cut-of sample out-of-sample alpha as the out-of sample
Predictor performance metric performance metric performance metric
10-year mean returns; 9 11 15
stars allocated by style
10-year mean returns; no 7 9 23
adjustment for styles
10-year Sharpe ratio 17 21 11
10-year single-index alpha 11 15 10
10-year 4-index alpha 5 5 17
Momingstar 15 13 11
Samples Examined: Same as Table 5.
Out-of-Sample Metrics Examined: Same as Table 5.
Test Examined: Same as Table 5.
*Significant at the 10% level.

TABLE 7
Out-of-Sample Performance—Coefficient Signs

# of cases out of 36 # of cases out of 36 # of cases out of 36
(12 samples; 3 out-of-sample (12 samples; 3 out-of-sample (12 samples; 3 out-of-sample
performance metrics) performance metrics) performance metrics)
in which the coefficient in which the coefficient in which the coefficient
signs are such that: signs are such that; signs are such that:
Predictor To (6-star) > T2 (3-star) T1 (4-star) > ^3 (2-star) T2 (3-star) > T4 (i-star)

10-year mean returns;
stars allocated by style 14(2) 28 (8) 34(27)
10-year mean returns;
no adjustment for styles 14(5) 21 (7) 33 (28)
10-year Sharpe ratio 30(9) 33(18) 35 (28)
10-year single-index alpha 20(3) 35(16) 33 (30)
10-year 4-index alpha 10(2) 20(13) 35 (29)
Momingstar 18(4) 29(19) 36 (24)
Samples Examined: Same as Table 5.
Test Examined: Same as Table 5.
Parentheses indicate the number of the cases in which the coefficiehts were as indicated and the difference in the coeffi-
cients was significant at the 10% level using a Wald Test.

Momingstar stars are again in the middle in terms of their success at predict-
ing future performance. Second, all the predictors, regardless of what type, have
more ability to predict low-performing funds. In at least 90% of the cases, the
72 (3-star) > 74 (1-star) condition is satisfied for every predictor. Third, all predic-
tors, with the notable exception of the Sharpe ratio, have problems in predicting
high-performing funds. For most predictors, the 70 (5-star) > 72 (3-star) condition
is satisfied 50% of the time or less.

Blake and Morey 477

2. Spearman-Rho Rank Correlation Results
Table 8 summarizes the Spearman-Rho rank correlation results for the al-
temative predictors.'^ The Spearman-Rho rank correlation tests are the same as
those in Section IV.A.3, except that we use the altemative predictors to rank the
funds instead of the Momingstar scores. Again we use the decile averages de-
scribed in Section III.D. There are six samples and three out-of-sample perfor-
mance metrics. Table 8's results show essentially the same findings as the dummy
variable results for the altemative predictors. The Momingstar scores are similar
in predictive ability to other altemative predictors. All the predictors, with the
exception of the Sharpe ratio, have much higher Spearman-Rho rank correlations
in the bottom five deciles than in the top five deciles, indicating that the predictors
forecast low-performing funds better than high-performing funds.

TABLE 8
Spearman-Rho Summary Results for Alternative Predictors and Momingstar Scores

#of cases (out of 18) #of cases (out of 18) # of cases (out of 18)
in which the in which the in which the
Spearman-rho rank Spearman-rho rank Spearman-rho rank
test is greater test is greater test is greater
than 0.5 across than 0.5 across than 0.5 across
Predictor all 10 deciles the top 5 deciles the bottom 5 deciles

10-year mean monthly returns; 2 1 5
no adjustment for styles
10-year Sharpe ratio 13 10 9
10-year single-index alpha 9 4 15
10-year 4-index alpha 5 0 5
Momingstar score 9 1 15
Samples Examined: Post 1993 Seasoned Fund Samples: 1994 (1- and 3-year). 1995 (1- and 3-year). 1996 (1-year), 1997
(1-year).
Test Examined: Spearman-Rho tests based on decile averages. We test all 10 deciles, the top 5 deciles, and the bottom
5 deciles. Since there are 6 samples with 3 out-of-sample performance metrics, there are 18 total tests for each predictor

B. Complete Funds 1993 Sample Results
Dummy Variable Regression Analysis
For the complete funds 1993 sample, the altemative predictors are the same
as those used above except that we use three years of in-sample data because the
young and middle-aged funds do not have the necessary 10 years of in-sample
data and all the funds must have a minimum of three years of historical retums
to be rated by Momingstar. The results for the altemative predictors using the
complete funds 1993 sample are presented in Tables 9-12, which provide the
same kind of information that Tables 5-7 provide for the 1992-1997 seasoned
funds sample. However, the number of cases is much smaller since we only have
three samples (rather than 12). The results show that, unlike those from the sea-
soned funds sample, the Momingstar star method does significantly better than
the altemative predictors at predicting future performance. Table 9 reports that.
^'We do not use the predictor in which we allocate ranking.s using mean monthly retums by their
.style because it is impossible to rank order all the funds using this method.

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