This document discusses using the Information Ratio (IR) to evaluate mutual fund managers. The IR measures excess return over a benchmark relative to excess return volatility. While commonly used, the IR has limitations that depend on benchmark choice, data frequency, and fund return distributions. The document aims to empirically analyze IR characteristics across different asset classes and countries to determine if it is a reliable performance measure or if guidelines are needed for its use.

1. How "Informative" Is the
Information Ratio for
Evaluating Mutual Fund
Managers?
THOMAS BOSSERT, ROLAND FUSS, PHILIPP RINDLER,
AND CHRISTOPH SCHNEIDER
reynor and Black's [1973] Infor- and that a focused asset class approach is nec-
T
THOMAS BOSSERT
is managing director mation Ratio (IR) is one of the essary. Moreover, the quality and reliability of
(portfolio management) at most commonly used performance the IR depends on certain estimation choices.
Union Investment Institu-
tional GmbH in Frankfurt,
measures. It represents the ratio of First, benchmark choice strongly affects
Germany. the excess portfolio return over a specified the ratio. Ideally, the benchmark should cover
thomas.bossert @union- benchmark, as well as excess return volatility. a large proportion of the respective investment
mtestment.de Closely connected is the fundamental universe. Second, data frequency should be as
!aw of active portfolio management (Grinold great as possible, because monthly data do not
ROLAND FUSS [1989]), which relates a fund manager's skills accurately represent return volatility. Third,
is a professor of finance and
holds the Union Investment
to the IR. This framework gives insights into non-normally distributed fund returns can
Chair of Asset Management how to use the IR to construct active portfo- substantially affect the use of the IR. Finally,
at European Business lios within predefined risk limits. In order for in order to separate lucky managers from
School (EBS), International investors to apply the ratio to a specific port- skilled ones, long-term track record can be an
University Schloss important measure.
folio choice problem, however, they need
Reichartshausen in
Oestrich-Winkcl. Germany.
guidelines to identify superior funds. The remainder of this article is orga-
roland.fuess@ebs.edu Grinold and Kahn [2000] state that top- nized as foUows, The next section, "The Infor-
quartile managers have IRs of at least 0.5, while mation Ratio," discusses the IR and its role
PHILIPP R I N D L E R exceptional managers achieve values above 1.0. within active portfolio management. The
is a research assistant at the These numbers are unqualified and should section after that, "Data Description," presents
Union Investment Chair of
hold irrespective of asset class, country, or time our dataset and explains our choice of funds
Asset Management at
European Business School period. To the best of our knowledge, IR char- and benchmarks. The empirical results are pre-
(EBS), hiternadonal acteristics across difierent asset classes and coun- sented next, in "Is the Information Ratio a
University Schloss tries have not been extensively studied yet. Reliable Performance Measure?", which
Reichartshausen in Hence, this article addresses whether the IR is begins by testing the IR for stability over time
Oesrrich-Winkel, Ciermany.
a useful and reliable performance ratio. It and across different fund categories. We then
ph¡lipp,rín(ller@ ebs.edu
focuses particularly on empirically observable discuss the robustness of the IR against the
CHRISTOPH SCHNEIDER quartile ranges for various asset classes and selection of different benchmarks and data fre-
IS an analyst at Morgan countries that investors can use as guidelines quencies. Finally, we examine the persistence
Stanley, Investment Banking to determine fund quality. of IRs over time in order to separate lucky-
Division in Frankñirt, We use return data fi-om nearly 10,000 managers from skilled ones. The final section
Germany,
mutual funds for the January 1998-December provides conclusions and su^esdons for future
christopli..schneider@ ebs.edu
2008 period. The empirical results show that research.
static breakpoints can be widely niisleading
SPRING 2010 THEJOURNAL OF INVESTING 67

2. THE INFORMATION RATIO Depending on the number of independent bets a man-
ager takes, different skill levels are required in order to
Treynor [1965] defines two characteristics of a achieve a "good" or "very good" IR (Wander [2003]).
"good" performance measure. First, it should provide the As we noted earlier, Grinold and Kahn [2000] define
same value for the same performance, irrespective of market IR levels on the basis of cost-adjusted fund performance.
conditions. Second, it needs to incorporate the preferences A top-quartile portfolio manager would have an IR of 0.5,
and risk aversion of investors. Similarly, Hübner [2007] and an exceptional manager would achieve a 1.0 or above.
states that there are two factors that determine the quality Again, according to this study, this classification should hold
of a performance measure: stability and precision. A stable for all asset classes and rime horizons, with only slight devi-
measure is robust under different asset pricing models, and adons. Jacobs and Levy [1996] also found an IR of 0.5 or
does not vary over time in terms of its classification. Pre- above to be "very good," without restrictions to asset classes.
cision means that it should be able to provide the "true" Goodwin [1998], on the other hand, analyzed the
ranking of funds based on investor preferences. IR distribution for samples of funds with different invest-
Treynor and Black [1973] define the IR as: ment universes, and found significantly different results
across fund categories. We believe this approach is more
ER plausible than the findings of the two other studies. Thus,
IR = -^ (1)
o. we expect to find different IR ranges in our empirical
analysis when evaluating funds that invest in different asset
where r is the portfolio return, r^ is the benchmark return, classes and countries.
ER is the excess remrn, and O^^ is the volatility of the excess
return. The rationale for the IR LS closely related to Jacobs DATA DESCRIPTION
and Levy's [1996] investor utility function. They explain
tliat investors in activefiandsare not risk-averse, but rather Fund Data
regret-averse. Regret aversion means generally accepting
the risk of a passive investment in this asset class, but— Our initial sample includes all actively managed, open-
depending on the excess returns—regretting the decision end funds listed for sale in Germany, the U.K., and the
to invest in an active fund. United States by Reuters 3000 Xtra as of February 2009.
Similarly, Grinold and Kahn [2000] find that investors Closed-end funds are excluded, because investors cannot
select among different opportunities based on their per- freely enter or exit them. REITs and hedge funds are also
sonal preferences, vhich, for actively managed funds, excluded, because their particular characteristics deniand
"point toward high residual return and low residual risk" specific performance measures that are not within the scope
(p. 5). Thus, by using the IR, investors can limit the fund of this article (see, for example, Ackermann et al. [1999] or
universe according to their personal risk preferences. Below and Stansell [2003]). We focus separately on equity,
Grinold [1989] identifies two factors that lead to fixed-income, and money-market funds because of their
high IRs. The first is the manager's ability to correctly differing risk-return characteristics. Furthermore, we
predict residual returns in his invesmient universe. Referred exclude balanced funds, because we aim to analyze and
to as the Information Coefficient (IC), it measures the characterize performance measures of distinct asset classes.
correlation between actual and forecasted alpha. The To categorize the funds, we use the Lipper Global Classi-
second, which describes the number of independent fication as it is used throughout Reuters 3000 Xtra.
investment decisions made per year, is called breadth. The In the equity class, we choose funds with a focus on
fundamental law of active management illustrates the rela- the major equity markets: Europe, Germany, the U.K.,
tionship between IR, IC, and breadth, as follows: and the United States. We also distinguish between large-
and small-cap funds {although the limited number of
small-cap equity funds in Germany resulted in the elim-
IR = IC (2)
ination of this category).
For purposes of this article, the crucial point about In the fixed-income class, we choose corporate
Equation (2) is that correct forecasting of residual returns investment-grade bond flinds with a focus on the British
should be a key skill of any active portfolio manager. pound, the euro, and the U.S. dollar, which are the three
major currencies for corporate bond emissions according
68 H o * "iNRmMATIVE" Is THE INFOKMATION P-ATIO FOR EVALUATING MUTUAL FUNU MANAGERS? SPRING 2010

3. Co Reuters 3000 Xtra. Finally, we use these same three Calculating the IR requires a market benchmark for
major currencies to select relevant funds in the money comparison. Fund managers normally define their bench-
market class. Ourfinaliund sample comisted of 9,632 funds.' marks in a prospectus. However, in light of the large
Our time frame ranges from January 1, 1998 number of funds and corresponding benchmarks within
through December 31, 2008. Our weekly return data, the same fund category, it was not possible to use each
launch years, and base currencies for the funds come benchmark to calculate the performance measures. Instead,
from Thomson Financial DataStream. We correct for we use a general benchmark for each fund category.
erroneous data entries by excluding funds with extreme Initially, this may seem somewhat unfair. But we
information ratios of above 20 or below —20. We also believe it is more logical to judge each fund within a cer-
exclude funds with launch dates after January 1,2007. tain investment universe against the same benchmark,
If a fund launched in the second half of a year, we set although it does introduce a bias into the analysis. Fund
the launch date to the next year in order to ensure a suf- managers that are actually managing their funds against the
ficient number of data points per calendar year for cal- benchmark tend to exhibit lower tracking errors, and
culating test statistics. For funds quoted in a currency therefore higher IRs, than managers using another bench-
other than the corresponding benchmark currency., we mark. They bear the tracking errors versus their true
convert the return data using the appropriate exchange benchmark plus the tracking errors between the true and
rate. Additionally, we retrieve daily and monthly return chosen benchmarks. Exhibit A2 in the Appendix provides
data for large-cap U.S. equity funds in our given time- an overview of the benchmarks assigned to the different
frame in order to analyze the influence of data frequency. fund classes.
However, because Reuten 3000 Xtra and Thomson
Financial DataStream only list funds that are currently Descriptive Statistics
available on the market, the data are subject to survivor-
ship bias. For us, this is especially relevant prior to 2007, Exhibit 1 gives the descriptive statistics for each fund
because only the funds that survived are contained in our category as well as the average excess return over the
dataset. We posit that the estimated performance measures respective benchmark. All numbers are annualized for
may be biased upward. In the "Other Influences on better comparability.
Performance Measures" section, we analyze the extent of In terms of risk/return relationships, we see that money-
and possible corrections for survivorship bias. market and corporate bond funds behave as expected. But
EXHIBIT 1
Descriptive Statistics of Fund Returns
Avg. Ann. Avg. Ann. Excess Avg. Ann.
Fund Classification Return Std. Dev. Skewness Kurtosis Excess Return
Equity Europe -0.72% 17.73% -0.539 2.885 -1.7!%
Equity Germany 0.18% 23.42% -0.418 3.484 -0.60%
Equity U.K. 1.97% 15.30% -0.722 3.167 0.68%
Equity U.S. -2.57% 18.23% -1.092 9.734 -3.22%
Equity Small Cap Europe 1.5!% 19.25% -0.986 2.816 2.50%
Equity Small Cap U.K. 4.09% 14.02% -1.223 3.020 2.27%
Equity Small Cap U.S. -2.54% 21.68% -1.151 9.119 -6.45%
Corporate Bonds EUR 2.38% 2.88% -0.666 3.914 -1.20%
Corporate Bonds GBP 3.65% 4.39% -0.572 2.662 -1.12%
Corporate Bonds USD 3.10% 4.22% -0.551 1.710 -1.58%
Money Market EUR 2.11% 0.30% -3.557 20.300 -0.25%
Money Market GBP 4.97% 0.45% 4.193 26.245 0.72%
Money Market USD 1.97% 3.12% 1.379 33.221 -0.93%
Note: Calculations are based on weekly data for thefanuaq' i 998—December 2008 period and are annualized.
SPRING 2010 THE JOURNAL OF INVESTING 69

4. the numbers for the equity segment are surprising- The poor for each fund category, not just in terms of value but also
performance of equities is due mainly to the impact of the in terms of range. A corporate bond fund with a positive
2008financialcrisis; Gainsfixim2003 to 2007 in the US. IR can usually be classified as "very good," while an Equity
equity market were completely erased in 2008. Europe Fund would only be average. Additionally, the
In terms of performance as measured by alpha, it is value range for a "good" Equity Europe fund is far nar-
clear that, over the 11-year period, managers in almost all rower than for a "good" Money Market EUR fund.
asset classes and fund categories were not able to beat the Nevertheless, the values and ranges within the a.sset
benchmark on average after costs. Note also that the classes seem similar. Further testing needs to be done to
money-market segment exhibits strong skewness and lep- confirm these results. But we find that general statements
tokurtosis. We will analyze the effects of non-normally about the IR, such as those of Grinold and Kahn [2000]
di.stributed returns on performance measures flirther in the (discussed earlier), are not applicable for all asset classes and
"Other Influences on Performance Measures" section. years because the threshold values vary considerably over
time. Exhibit 3 shows detailed information about how the
IS THE INFORMATION RATIO A RELIABLE threshold values develop over time for the top quartiles.
PERFORMANCE MEASURE? But are these strong IR fluctuations statistically sig-
nificant? To test for this difference, we calculate the median
The Distribution of the Information Ratio [R of the top half of all Equity US. funds for each of the
11 years, i.e., the threshold value between the first 25%
To analyze whether the distribution of [Rs is stable and the second 25% of the funds. We then test this value
over time and across different fiand categories, we rank each year to see if it is statistically significantly different
the ratios for each year and asset class, and then divide from the average threshold value reported in Exhibit 2.
them into four quartiles. We use a Wilcoxon signed-rank The results are outlined in Exhibit 4, with the
test and an optional student r-test to test the yearly values threshold values in the first data row and the z-statistics
against the overall average for statistically significant in the second row. We again use the Wilcoxon signed-rank
differences. We present all results in annualized form for test because the IRs are not normally distributed according
better readability and comparability by using arithmetic to the Lilliefors test, and we assume they are dependent
mean returns according to Goodwin's [ 1998] method 1 .' on each other (see Hollander and Wolfe [1973]).
Exhibit 2 presents the threshold values for the four The results in Exhibit 4 clearly show that the
quartiles, which are averages over the 11-year horizon ot threshold values are significantly different from the
the dauset. Note that the IRs exhibit very different patterns 11-year average in every year. A look at the z-statistics
EXHIBIT 2
Information Ratios of Different Fund Categories
IR 1st 25% IR 2nd 25% IR 3rd 25% IR 4th 25%
Fund Classification «Very Good" «Good" "Below Avg." "Poor"
Equity Europe >0.40 0.40 to 0.04 0.03 to -0.36 <-0.36
Equity Germany >0.07 0.07 to-0.11 -0.12 to 0.37 <-0.37
Equity U.K. >0.32 0.32 to-0.01 -0.02 to -0.30 <-0.30
Equity U.S. >0.28 0.28 to -0.40 -0.41 to-I.Ol <-1.01
Equity Small Cap Europe >0.80 0.80 to 0.40 0.29 to -0.09 <-0.09
Equity Small Cap U.K. >0.59 0.59 to 0.22 0.21 to-0.12 <-0.12
Equity Small Cap U.S. >0.08 0.08 to -0.60 -0.61 to-1.18 <-I.18
Corporate Bonds EUR >-0.24 -0.24 to -0.76 -0.77 to-1.30 <-1.30
Corporate Bonds GBP >0.03 0.03 to -0.46 -0.47 to -0.95 <-0.95
Corporate Bonds USD >0.03 0.03 to -0.58 -0.59 to-1.29 <-1.29
Money Market EUR >4.30 4.30 to 1.36 1.35 to-0.39 <-0.39
Money Market GBP >4.30 4.30 to 0.31 0.30 to-1.50 <-1.50
Money Market USD >2.46 2.46 to 0.39 0.38 to-1.29 <-1.29
70 How ••INK>HM.TIVE" IS THE RATK) FOR EVALUATING MUTUAL FUND MANAGERS? SPRING 2010

5. EXHIBIT 3
Information Ratio—Threshold Values for 1st Quartile Funds (very good)
Fund Classificatioii 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Equity Europe >0.23 >Î.3O >0.38 >0.I5 >0.28 >-0.12 >0.46 >0.91 >0.81 >-0.09 >0.08
Equity Gennany >0.02 >-0.16 >0.44 >0.2I >0.35 >0.I2 >-0.14 >-0.02 >0.08 >-0.22 >0.11
Equity U.iC >-0.26 >0.79 >0.62 >0.24 >0.15 >0.65 >0.44 >0.31 >0.58 >-0.23 >0.I8
Equity U.S. >-0.39 >0.36 >0.66 >0.51 >0.71 >0.36 >0.18 >0.55 >-0.38 >0.44 >0.08
Equity Small Cap Europe >0.04 >2.40 >0.60 >-0.21 >0.50 >I.4O >1.70 >1.60 >1.60 >-0.28 >-0.49
Equity Small Cap U.K. >-0.92 >2.50 >0.67 >0.19 >0.25 >1.30 >1.40 >0.47 >1.50 >-0.68 >-0.18
Equity Small Cap U.S. >0.65 >1.50 >-0.26 >-0.21 >0.06 >0.44 >-0.74 >-0.05 >-0.49 >0.49 >-0.52
Corporate Bonds EUR N/A N/A N/A N/A >0.08 >-0.30 >-0.95 >-0.32 >0.26 >0.63 >-1.10
Corporate Bonds GBP >0.56 >-0.04 >-0.64 >-0.50 >-0.19 >-0.17 >0.07 >-0.15 >-0.08 >0.30 >1.20
Corporate Bonds USD >-0.37 >0.26 >0.46 >-0.64 >-0.28 >-0.01 >-0.26 >-0.08 >0.00 >0.49 >0.71
Money Market EUR N/A N/A N/A N/A >4.60 >7.70 >7.40 >7.80 >4.20 >0.51 >0.04
Money Market GBP >0.33 >1.10 >1.20 >4.60 >5.90 >5.60 >3.70 > 10.00 >7.90 >3.80 >3.20
Money Market USD >I.8O >1.50 >1.40 >5.90 >3.00 >5.20 >2.70 >0.98 >2.10 >1.30 >1.20
EXHIBIT 4
Test Statistics for the Difference of Threshold Values of Equity U.S. Funds
Wilcoxon Signed-RankTest on Differences in Mean
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Avg.
-0.39* 0.36* 0.66* 0.5 r' 0.71* 0.36* 0.18* 0.55* -0.38* 0.44* 0.08* 0.28
-16.5 -3.0 -15.4 -12.5 -19.8 -9.0 -3.2 -20.7 -34.3 -15.4 -4.0
Note: ^Denotes values significantly different from average at the 5% significance level. AU test statistics for the Ulliefors test for normality are significant at the
5% level. The test is a generalization of the Kolmogorov-Smirnov (KS) test, which requires specification of the population mean and mriatue. The Ulliefors test
is capable of testing samples for normality thai hatv incompletely specified distribution characteristics (Ulliefors f1967}).
reveals that the values are statistically different from their across different countries. The procedure is exactly the
average. This is also highlighted by the spread in threshold same as in the previous test on Equity U.S. funds; results
values, from -0.39 in 1998 to 0.71 in 2002. Thus, a fund are in Exhibit 5.
evaluated on the yearly threshold value could be catego- Similar to the results in Exhibit 4, Exhibit 5 shows
rized as "below average " while simultaneously being rated that all threshold values are significantly different from
as "very good" on the overall average value. their averages. This statement is valid for 1998 and for
To conclude, we believe IRs must be calculated anew 2008, so we consider it rather robust. IRs thus not only
each year in order to be reliable. Because the relevant change over time, but also between different fund cate-
thresholds can only be calculated ex post, it is not possible gories. And we cannot confirm the general statements
to use IRs when setting annual targets for fund managers. about fixed threshold values found in Grinold and Kahn
However, in the context of a multi-year planning process, [2000] or Jacobs and Levy [1996].
long-term IRs might be applicable. The results of this part of our empirical study are
In the next step, we studied IRs across different similar to the results of Goodwin [1998]. with the addi-
fund categories. Our focus was again on U.S. funds, as it tion that IRs also change over time. Exhibit 6 uses box-
seems more likely that we will find similar IRs when and-whiskers plots to graphically illustrate the different
looking at several asset classes within one country than distributions of IRs over time for Equity U.S. funds.
SPRING 2010 THE JOURNAL OF INVESTING 71

6. EXHIBIT 5
Test Statistics for the Difference of Threshold Values of U.S. Funds
Year Equity Small Cap Equity Fixed-Income Money Market Average
Wilcoxon -0.39* 0.65* -0.37* 1.80* 0.42
z-score -17.53 -6.39 -5.51 -3.82 -
Wilcoxon 0.08* -0.52* 0.71* 1.20* 0.37
z-score -10.10 -26.66 -5.17 -4.04 -
Note: *Denotes values significantly different from average at the 5% significance level. Similarly to the previous test, the IRs are not normally distributed
according io the LilUefors test, and all test statistics are significant at the 5% level. Tlw second row gives ¡he z-scores of the Wilcoxon signed rank test.
EXHIBIT 6
Box Plots of Equity U.S. Fund Information Ratios
Equity US Funds
T T
T t i
1 t-
t
2ooe
The Art of Selecting a Benchmark funds. But we will use two additional indices to compare
the resulting IRs, the equally weighted Dow Jones Indus-
In fund management companies, benchmark selec- trial Average (DJIA) and the market-weighted Russell
tion is usually the result of intense negotiations between 1000 Index. Exhibit 7 presents the threshold IRs for dif-
the fund manager and the investors, because it has a major ferent benchmarks using the same procedure as in the
impact on the fund's alpha. Depending on the style and previous section.
country focus of a fund, one benchmark might be much Note that the IRs based on the S&P 500 and the
more favorable to a flind manager than another (Goodwin Russell 1000 are closely related, while the IRs based on
[1998], Grinold and Kahn [2000]). Therefore, it is impor- the DJIA behave differently and are far more volatile.
tant to analyze the sensitivity of the IR toward the selected It appears that the DJIA does not cover the Equity U.S.
benchmark. investment universe very well. This may be because this
Lehmann and Modest [1987] show that benchmark index is based on only 30 stocks.
selection strongly influences the resulting alphas as well We again use the Wilcoxon signed-rank test to test
as their volatility. Thus far, we have used the S&P 500 for significance of the difFerence in threshold values.
throughout this article in connection with Equity U.S. Exhibit 8 gives the results from the Russell 1000 and the
72 How "INFORMATIVE" IS THE 1NFORJ«IATION P ^ T I O FOR EVALUATING MUTUAL FUND MANAGERS? SPRING 2010

7. EXHIBIT 7
The Effect of Benchmark Selection on the Information Ratio
- Dow Jones Industrial Average
S&P 600
Russell 1000
2005 2006 2007 2006
EXHIBIT 8
z-Statistlcs for Significant Difference of the Infonnation Ratios
z-Values for 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
D o w Jones -18.1* -9.6* -9.3* -22.0* -26.6* -26.4* -30.9* -32.9* -7.5* -8.6* -25.3*
Russell 1000 -9.4* -4.2* -3.5* -14.2* -8.5* -17.1* -25.0* -12.7* -31.9* -21.1* -33.5*
Note: *Demtes values significantly different from average at the 5% significance level. Similarly to the previous test, the IRs are not normally distributed
according to the LiUiefors test, and all test statistics are significant at the 5% level.
DJIA versus those from the S&P 500. All are significantly fund category are superior to those based solely on a few
different from those based on the S&P 500 (at a 5% sig- securities and industry sectors. Finally, the best way to
nificance level). These results are in line with Goodwin judge the real risk-adjusted value-added of a fund man-
[1998], who also found that benchmark selection strongly ager is to consider his actual benchmark, as well as the
influences the resulting IRs. true benchmarks of the other managers within his peer
The scatter plots in Exhibit 9 illustrate the rankings group. Or, as an alternative, we could use the peer group s
based on the three different IRs. We can see that the results average as a general benchmark, which might lead to more
are confirmed: There are noticeable differences between stabiUty in the annual IR thresholds.
IRs based on the DJIA and those based on the S&P 500, In the same sense that benchmark selection is so
while the changes in ranking between the Russell 1000 critical, investment restrictions are also quite important.
and the S&P 500 are quite small. Selecting an appropriate The Transfer Coefficient (TC) measures the correlation
benchmark is therefore an important step during perfor- of a manager's forecasts with the actual portfolio. A man-
mance analyses in general. ager without constraints will end up with a TC of 1, while
However, we conclude that benchmark indices that the constrained manager can only achieve a lower result.
capture a larger part of the investment univeree of a specific Furthermore, a typical long-oniy fund may achieve a TC
SPEUNG 2010 THE JOURNAL OF INVESTING 73

8. EXHIBIT 9
Ranking Differences Caused by Different Benchmarks
200 doo eoo BOO 200 400 600 eoo 1000
Rank by Irtornistion Ralio (S&P 600) Rank by mtoimalion ñatn (S&P 600)
between Ü.2 and (.).4, which would significantly impact the monthly data are inappropriate for calculating reliable
IR because it implies the manager is not able to fully performance measures. Monthly data also allow for only
transfer his skills into actual investment decisions. 12 data points per year, which is insufficient to estimate
Assuming a (constrained) fund with a TC of 0.5, a the standard deviation.
manager must double his skill (IC), or quadruple his
breadth, in order to achieve the same IR as an uncon- Other Influences on Performance Measures
strained manager for an equal fund (Wander [2003]).
Many studies have documented that returns are gen-
Does Data Frequency Matter? erally non-normal. However, many popular performance
measures are still based on mean-variance analysis. There-
Ané and Labidi [2íK)4] have shown that the return fore, as per Benson et al. [2008], we find that non-normal
interval has a significant impact on the return distribution. returns lead to biased results.
Monthly and quarterly returns come close to a normal dis- Additionally, Kraus and Litzenberger [1976] found
tribution, but weekly and especially daily data usually that positively skewed returns are actually favorable for
show strong leptokurtosis. Furthermore, the annualized investors. If we refer back to Exhibit 1, which presents
standard deviation varies with frequency. descriptive statistics of fund returns for each category, it
Other research has also found that data frequency is striking that money-market funds in all currencies pro-
influences correlations (Handa et al. [1989]). But we need duce strongly skewed and leptokurtotic returns. Com-
to determine whether data frequency also impacts the IR paring the threshold values in Exhibit 2, the values for
and, in particular, the threshold values. "top" money-market funds are uncharacteristically high.
If we do fmd significant influence, we aim to Thus, we conclude that common performance measures
describe these differences and to provide guidance tor are not applicable to these fiinds, probably because of their
selecting an appropriate return interval. Thus, we calcu- special return distribution characteristics. Other perfor-
late annualized IRs for Equity U.S. funds using daily, mance measures, such as Keating and Shadwick's [2002]
weekly, and monthly fund returns. Using the ranking Omega Measure, could capture the deviation from
methodology explained earlier, we create fund rankings normality.
based on three different IRs. Another important factor is the survivorship bias that
Results for the year 1999 are given in Exhibit 10. can result because the most common data providers only
We see that the rankings of IRs based on daily and weekly list currendy available funds. This may lead to an upward
data do not differ significantly, but switching to monthly bias in the performance measure estimates. Brown et al.
data changes the ranking dramatically. We conclude that [1992] found that survivorship bias can be so strong that
74 How "INRIRMATIVE" IS IHE INR)RMATION RATIO FOR EVALUATING MUTU.^L FUND MANAGER.S? SPRING 2010

9. EXHIBIT 10
Comparison of Rankings Based on Different Data Frequencies
200 400 600 600 1000 1200 200 '100 SOG 800 !000
Rar* by Infomi^ion Haio (Ftequency: Vi&l REV* by trtomialKin R^io (Frequency: Weekly)
it can lead to the erroneous conclusion that mutual fund Eliminating survivorship bias may lead to lower real
performance is predictable. However, when we correct the average IRs, but costs and asynchronous pricing have a
sample for survivorship bias, this finding disappears. negative impact. Including these two factors will lead to
In terms of quantifying the survivorship bias for somewhat higher average IRs.
Equity U.S. funds, Grinblatt and Titman [1989] found a
O.iyo-0.3% bias per year. Brown et al. [1995] estimated the Performance Persistence: Outperformance
bias at between 0.2% and 0.8% per year, while Elton et al. by Luck or by Skill?
[1996] posited an average bias of O.7r/o-O.77% annually.
Although it is very likely that survivorship exists in our Finally, we question whether a single ratio based on
dataset, we were unable to quantify its proportions. How- one year of data is sufficient to evaluate a manager's per-
ever, because our study is set up similarly to the previously formance. It is important to determine whether achieving
cited studies, we believe our results would be subject to a high IR in any given year was attributable to luck or
a similar order-of-magnitude of bias. actual skill.
Costs and asynchronous pricing are two other fac- Here, the managers track record can be a usefU tool.
tors that influence our results. In order to estimate the The probability that good performance is attributable to
real risk-adjusted value-added of a fund manager, we need skill increases when managers can position their tunds
to compare his results net of fees with those of other fund among the top 25% for two or three years in a row (Bollen
managers. However, we also need another dataset to com- and Busse [2005]). However, care should be taken when
pute thosefigures.And fijnd investors may be more inter- trying to predict fiiture fijnd returns based on past returns.
ested in the final result than in whether the results were Horst and Verbeek [2000] show that some studies
due to skill or the cost structure of the product. claiming to find performance persistence are likely
The asynchronous pricing introduces an upward reporting spurious and biased results. Kahn and Rudd
bias into the tracking error. For example, a fund that is [1995] and Carhart [1997] also analyzed persistence of
tracking its benchmark perfecdy, but whose NAV is not equity mutual funds, and did not find a significant rela-
calculated with the same security prices or foreign tionship betw^een past and future performance.
exchange rates as the benchmark, will inevitably exhibit We fmd similar results with our dataset. We catego-
a tracking error that is different from zero. Again, quan- rize Equity U.S. funds launched in 1998 or before into
tifying this bias would require a dataset that draws heavily quartiles based on their 1998 IR. For each quartile, we cal-
on the internal valuation information of a fund manage- culate the average IRs for each year. The results are shown
ment company, w^hich is difficult to coine by. in Exhibit 11.
Si'KING 2010 THEJOURNAL OF INVESTING 75

10. EXHIBIT 11
Performance Persistence of Equity U.S. Funds
4th Quartile
3rd Quartile
2ncl Quaiiile
1st Quartile
-2.6
1998 1999 2006 2007 2008
Note that the top-quartile funds of 1998 actually the top quartile for two or three years in a row for rolling
have the lowest average IR afiier two years. The chart sug- three-year periods, and the results are fairly stable and
gests a mean-reverting process and shows that, on average, consistent.
good performance does not persist. Based on this fact, we We interpret Exhibit 13 as follows. When looking
conclude that lucky managers without skill are not likely at the first row, for the 2008-2006 period, 0.93% of all
to remain among the best funds for multiple years in a row. Equity U.S. funds were in the top 25% of the funds in all
We therefore propose track record as a second three years. For the 2008-2007 period, 2.33% of all Equity
dimension to evaluate manager performance. First, we U.S. funds were in the top 25%, and for 2008, the number
track the performance of fiinds from selected categories was 21.73%. These three values add to 25% with only
that launched in 1998 or earlier and survived until 2008 minor rounding differences.
over the entire 11-year period. Then we calculate the We perform the same calculation using the top 50%
number of years that each flmd ranked in the top 25%^ of funds. However, we conclude that the top 50% is
within this period. Exhibit 12 presents our summarized achieved too easily, and is therefore not an appropriate
results, which are as expected. measure.^ Based on these results, performance persistence
Note that, during the 11-year period, 95.5% of all (the track record of a manager) is another important factor
Equity U.S. funds and 93.4% of all Equity Small Cap U.S. in separating luck from skill in performance measure-
funds were in the top 25% at least once. Hence, if a fund ment. Investors should thus seek a fund manager with a
survives for 11 years, it is likely to be in the top quartile consistent series of performance measures, rather than
in some years just by luck. However, it is important to what could be unrelated episodes of good performance
note that these results could be caused at least partly by over longer timeframes.
changes in fund management.•*
Taking the results from Exhibit 12 one step further, Agency Problems
we calculate how many fiands remain in the top quartile
for two or three years in a row within a three-year period The agency problem can be illustrated as follows.
based on their IRs. According to Exhibit 13, such funds Consider a portfoho manager who makes just one active
would be considered extraordinary, since, on average, only investment decision per year, and whose correlation
2.76% of all funds have managed this achievement. between forecasted and actual returns is 0.1. According to
We calculate the percentage of all fiinds that remain in the fundamental law of active management, this manager
76 How "INFORMATIVE" IS THE INFORMATION RAno FOR EVALUATING MUTUAL FUND MANAGERS? SPRING 2010

11. EXHIBIT 12
Number of Top 25% Rankings Over Lifetime
Fund Classificstion 0 1 2 3 4 5 6 or more
Equity U.S. 4.5% 12.8% 21.4% 24.9% 17.7% 1A% 6.3%
Equity Small Cap U. S. 6.6% 11.0% 21.7% 24.4% 18.3% 9.3% 8.7%
EXHIBIT 13
Perfonnance Persistence of Equity U.S. Funds Over Time
Top 25% ... Years i a Row
in Top 50% ... Years in a Row
Period lYear 2 Years 3 Years lYear 2 Years 3 Years
2008 to 2006 21.73% 2.33% 0.93% 25.73% 11.58% 12.67%
2007 to 2005 20.41% 2.88% 1.72% 28.22% 7.81% 13.94%
2006 to 2004 18.77% 3.04% 3.18% 26.48% 5.51% 17.99%
2005 to 2003 16.76% 4.07% 4.15% 20.87% 9.32% 19.81%
2004 to 2002 14.80% 7.57% 2.65% 18.20% 14.23% 17.54%
2003 to 2001 19.76% 2.33% 2.87% 25.49% 6.51% 17.97%
2002 to 2000 12.10% 5.15% 1.11% 13.22% 6.87% 29.87%
2001 to 1999 11.11% 13.17% 0.72% 10.66% 29.66% 9.68%
2000 to 1998 23.35% 0.83% 0.83% 34.09% 5.79% 10.12%
Mean 17.64% 4.60% 2.76% 22.55% 10.81% 16.62%
vill achieve an IR of 0.1. The empirical part of this article mutual fund managers. Based on empirical evidence, we
shows that an IR of 0.1 for an Equity U.S. fund would in find that the IR is in fact reliable and useful, but has certain
most years be considered "good," and in some years even limitations. Overall, our analysis reveals that two dimen-
"very good," despite the fact that the manager may have sions are important to adequately judge manager perfor-
done very little. We believe the IR can potentially incen- mance in a given year: 1) the performance in that year,
tivize strategies that may he unfavorable to investors. It and 2) the track record of the fund over the previous three
seems that performance measures that use the tracking years. The former can be used to establish a ranking of
error as a risk measure need a second dimension that cap- funds that are then adjusted either upward or downward
tures the active weights of the fund, such as the Active by the latter.
Share measure proposed by Cremers and Petajisto [2009]. In order to transform the IR into a grading system,
This measure is easy to calculate and can quantify the we introduce a categorization of quartiles that define
active holdings of a mutual fund in relation to the corre- thresholds of fund qualities. IRs vary over time and also
sponding benchmark. across different fund categories, so it is necessary to cal-
culate threshold values anew for every calendar year. This
CONCLUSION makes the IR a difficult choice when setting targets for
portfolio managers, because they will not know how well
Practical Implications they must perform until the end of the year.
We found that four factors influence the quality
The aim of this article is to evaluate whether the of the IR: 1) benchmark selection, 2) data frequency.
IR is a useful and reliable measure of the performance of
SPRING 2010 THE JOURNAL OF INVESTING 77

12. EXHIBIT 14 biased. Performance should be (and in actual
Framework for Performance Evaluation—Year 2008 practice is) measured using returns net of
fees. In fact, a significant part of the total fees
-1.0 -0.5 0.0 0.5 1.0 1.5 cannot be influenced by the portfolio man-
agers, e.g., fund audit or custody fees.
Equity Euro Second, the sample is dominated by
U.S. funds simply because of the data
providers we used. Third, many funds are
Equity Germa
subject to style drifts, which generally make
returns harder to compare (Chan et al.
Equity U.K. [20ü2]). Although we selected very broad
fund categories, it would be interesting to
test for biases caused by style drift.
Fourth, the sample is subject to sur-
orate Bonds GBP vivorship bias of up to 0.8% per year. This
distorts the performance measures calculated
Corporate Bonds USD on these returns (Brown et al. [1995]). Fifth,
asynchronous pricing might result in tracking
"below average" and "poor" "good" • "very good error estimates with an upward bias, and
therefore to IRs that are lower than the real
3) non-normality of fund returns, and 4) any sur- IRs.
vivorship bias inherent in the sample used to estiinate In addition to our dataset, the analyses creates ideas
the threshold values. Regarding the benchmark, we for additional research. For example, we would recom-
recommend selecting an index that captures a large part mend comparing the results based on a generic bench-
of the respective market. The data frequency should be mark with results based on fund-specific benchmarks as
high—daily or weekly. Returns should also be tested determined by the portfolio manager. Alternatively, use of
for normahty, as this influences the quality of the per- the peer group average as a benchmark might lead to
formance measures significantly. Finally, quantifying the more stability in the wildly fluctuating [Rs.
survivorship bias within the IR is difficult and still Another suggestion is to analyze the IRs ot tunds
unclear. Thus, it is best left for iliture research. Note that with more specific style definitions, such as "U.S. value
the proposed framework is only valid for funds with stocks" or "European bank stocks." However, the number
symmetric return profiles. of these funds is rather small, which may render the results
Exhibit 14 is an example of a pertorniance evalua- insignificant.
tion framework based on the IR that is calculated using Finally, the effect of the Transfer Coefficient on a
the dataset of our empirical study. It is valid for funds of manager's active performance should be analyzed in more
the selected categories in 2008 and can help estimate per- detail. Fund managers face certain investment restrictions
formance along the first dimension, the performance of that prevent the allocation of funds to the best possible
the fund within a particular year. We make no differen- portfolio. These restrictions will negatively affect the IR,
tiation between funds belonging to the third or fourth although they are not influenced by the manager.
quartiles ("below average" or "poor" funds), because their According to Wander [2003], mutual tlinds can face TCs
IRs are mostly negative and thereiore unreliable. of 0.5 or even lower, and therefore managers would have
to double performance to obtain results comparable to
unconstrained portfolio managers. Future research could
Further Research
develop and empirically analyze ways to modify perfor-
While our results answer many of the research ques- mance measures so that the impact of investment restric-
tions, they also open up new issues. First, the returns are tions is neutralized across funds.
not corrected for fees, so the performance is somewhat
78 H o * "INKIRMATIVE" Is THE INFORMATION RATIO FOR EVALUATING MUTUAL FUND MANAGERS? SPRING 2010

13. APPENDIX
EXHIBIT Al
Sample Size of the Fund Dataset Grouped by Fund Classification
Number of Funds in the Dataset by Year
Fund Classification 1998 2000 2002 2004 2005 2006 2007/08
Equity Europe 127 214 363 553 689 813 895
Equity Germany 54 57 65 70 73 80 84
Equity U.K. 189 267 370 514 570 658 681
Equity U.S. 970 1,341 2,117 2,832 3,203 3,648 3,953
Equity Small Cap Europe 31 64 98 132 152 184 202
Equity Small Cap U.K. 51 67 83 109 111 127 132
Equity Small Cap U.S. 529 775 1,237 1,653 1,842 2,057 2,184
Corporate Bonds EUR 0 0 49 129 151 171 185
Corporate Bonds GBP 50 86 124 167 187 211 222
Corporate Bonds USD 88 108 158 203 211 231 237
Money Market EUR 0 0 164 223 243 283 300
Money Market GBP 36 53 79 94 99 112 118
Money Market USD 202 230 320 396 410 433 439
Source: Aggregation based on Reuters 3000 Xtra and Tliotnson Financial DataStream.
EXHIBIT A2
Overview of Benchmark Indices
Fund Classification Benchmark Name DataStream Ticker
Equity Europe MSCI Europe "MSEROP
Equity Germany DAX DAXINDX
Equity U.K. FTSE 100 FTSE100
Equity U.S. S&P 500 S&PCOMP
Equity Small Cap Europe MSCI Europe MSEROP
Equity Small Cap U.K. FTSE All Share FTSEALLSH
Equity Small Cap U.S. S&P 600 Small Cap S&P600I
Corporate Bonds EUR iBoxx Liquid EUR Corporates IBELCAL
Corporate Bonds GBP iBoxx Liquid GBP Corporates IB£CSAL
Corporate Bonds USD Merrill Lynch Corporate Master MLCORPM
Money Market EUR EUR Interbank 3M Offered Rate BBEUR3M
Money Market GBP GBP Interbank 3M Offered Rate BBGBP3M
Money Market USD USD Interbank 3M Offered Rate BBUSD3M
Source: Thomson Financial DataStream.
SPRING 2010 THE JOURNAL OF INVESTING 79

14. ENDNOTES Cremers, M., and A. Petajisto. "How Active is Your Fund Man-
ager? A New Measure That Predicts Performance." Working
'See Exhibit AI in the Appendix for a complete overview Paper, Yale School of Management, New Haven, 2009.
of the ftind types analyzed here.
""The reported results were not sensitive to the use of Elton,E.J.,M.J. Gruber, and C.R. Blake. "Survivorship Bias and
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