SUMMER 2010 THE JOURNAL OF WEALTH MANAGEMENT 75

The German mutual fund industryis a €575 billion business, with atotal of more than 6,000 fundsavailable (BVI [2009a]). The

volume of assets under management has grown36-fold over the last two decades, and thenumber of funds has increased 60-fold (BVI[2009b]).

The breadth and complexity of themutual fund market has become so large thata service industry has emerged to aid investorsin identifying the relative quality of singlefunds.Fund ratings quantitatively measure pastperformance and assess differences in fundquality (BVI [2007]). The efficient markethypothesis suggests that evaluations based onpast data should not be used to predict futurequality. However, the fact that highly ratedfunds are advertised so widely suggests thatinvestors do consider ratings important (Jainand Wu [2000]).

This idea has gotten the attention of aca-demic research. The mutual fund rating lite-rature has specifically aimed to answer twoquestions:

1. To what extent do investors perceive thatfund ratings predict the future quality ofa fund? (See Damato [1996]; Sirri andTufano [1998]; Del Guercio and Tkac[2001].)

2. To what extent do fund ratings actuallypredict future performance? (See Khorana

and Nelling [1998]; Loviscek and Jordan[2000]; Duret et al. [2008].)

Although prior studies have provided sig-nificant insight into both questions, the majorityof the literature has focused on the U.S.market.The influence of ratings on the German markethas not yet been explored. The German fundmarket,with its strong bank dominance,closed-end architecture, and low ratio of risky prod-ucts, is structurally very different from the U.S.market (Rekenthaler et al. [2009]). Conse-quently, studying this market would not onlyshed light on the behavior of German investorsbut also provide insight into how different reg-ulatory structures influence the fund market.

This article analyzes the two questionsand provides a first step toward determininghow fund ratings influence the German mutualfund market. We first use post-rating perfor-mance to investigate to what extent Germanfund ratings identify quality differences amongfunds. Next, we examine whether Germaninvestors use ratings as a measure of a fund’sfuture quality and hence allocate higheramounts into higher-rated funds.

For comparability with other studies,we analyze both questions on the basis ofMorningstar ratings. The timeframe of ourstudy is May 2004 through April 2009, toincorporate both high-volume up-markets andhigh-volume down-markets.

From Rising Stars and FallingAngels: On the RelationshipBetween the Performance andRatings of German Mutual FundsROLAND FÜSS, JULIA HILLE, PHILIPP RINDLER, JÖRG SCHMIDT,AND MICHAEL SCHMIDT

ROLAND FÜSS

is a professor of finance atthe European BusinessSchool in Oestrich-Winkel,Germany.roland.fuess@ebs.edu

JULIA HILLE

is an analyst at Credit SuisseEquity Sales in Frankfurt,Germany.julia.hille@credit-suisse.com

PHILIPP RINDLER

is a research assistant at theEuropean Business Schoolin Oestrich-Winkel,Germany.philipp.rindler@ebs.edu

JÖRG SCHMIDT

is a portfolio manager atUnion Investment Institu-tional in Frankfurt,Germany.joerg.schmidt@union-investment.de

MICHAEL SCHMIDT

is a managing director ofUnion Investment – PrivateFunds in Frankfurt,Germany.michael.schmidt@union-investment.de

JWM-FUSS:JWM-FUSS 4/19/10 4:50 PM Page 75

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MANAGEMENT AND RATINGS

A fund rating is a quantitative assessment of a fund’squality measured in a prespecified way. Regardless of themeasurement process, the outcome has a value compo-nent that distinguishes between high- and low-qualityfunds. However, the measure is calculated ex post and thusonly ranks past performance (Del Guercio and Tkac[2001]). Consequently, we need to determine how fundratings add value for investors.

Fund ratings are generally considered valuable intwo ways: 1) as a way to aid investors in categorizing theportfolio landscape and/or 2) as an indicator of futureperformance.

The notion that ratings can assist investors in cate-gorizing fund investments seems reasonable. Given thesheer amount of portfolios available to investors, as wellas the trend toward multi-portfolio investing, typifyingfunds can certainly help narrow this pool (Morningstar[2007]). However, if this is the primary value for investors,then the actual quality measurement should not influencepurchase decisions today or in the future.

But investors may also assume ratings contain someforecasting ability. This leads to two fundamental questions:1) Do investors use ratings as a decision-making toolwithin their fund selection process and 2) does using rat-ings to choose funds actually help investors find better-performing funds?

Morningstar Ratings

The three major rating agencies in the German fundmarkets are Lipper, Morningstar, and Standard & Poor’s(Atzler et al. [2009]). Because their measures differ, how-ever, they often come to different conclusions about fundquality. Morningstar offers ratings on the largest numberof funds. It is thus the first choice for cross-sectionalanalysis.

Morningstar groups funds into categories and thencalculates risk-adjusted performance measures. The fundsin each category are ranked and assigned a rating fromfive stars (best) to one star (worst). Morningstar subcate-gorizes its five broad asset classes (allocation, alternative,equity, fixed income, and money market) into more than180 subcategories of funds with similar styles. This assess-ment is based on 1) investment type, 2) the idea that astyle group should be comparable to a single benchmark,and 3) the idea that the funds within one category can be

considered reasonable substitutes for each other within aportfolio (Morningstar [2002]).

The Morningstar risk-adjusted return (MRAR) iscalculated in four steps. The total return of a given port-folio in month t is first calculated as:

(1)

where Pe and Pb are the net asset values (NAV) of thefund at the end and the beginning of month t, respec-tively; Di is the per share distribution, which can be in theform of dividends, capital return, or distributed capitalgains; Pi is the amount of NAV reinvested per share attime i; and n is the cumulative number of distributionsduring month t.

The second step is to adjust the returns for any loadsor fees. Note that when this new methodology was intro-duced,1 Morningstar did not tout the adjustment muchwithin the remodeled rating process. However, this is asignificant improvement. Both practitioners and acade-mics had previously noted a bias in the results due to thelack of adjustment.2 The load-adjusted return for montht is thus:

(2)

where F is the maximum front load, R is the redemp-tion, D are the deferred loads, all expressed as decimalsfor time t, P0 is the beginning-period NAV per share, andPt is the ending-period NAV.

In the third step, excess returns are calculated as:

(3)

For Europe, the risk-free rate depends on the fundscategory and is either the Merrill Lynch one-monthLIBOR index or the three-month Treasury bill.

In a fourth step, Morningstar adjusts the excessreturns for risk aversion by stating that the certainty equiv-alent of the excess return is equivalent to the excess returnadjusted by risk aversion:

ERLR

RFtt

t

=++

−1

11

LR TR F R D FP P

Pt t t tt= + − − − − −( )( )( ) ( )

min( , )1 1 1 1 0

0

11

TRP

P

D

Pte

b

i

ii

n

= +⎛

⎝⎜⎞⎠⎟

⎧⎨⎪

⎩⎪

⎫⎬⎪

⎭⎪−

=∏ 1 1

1

76 FROM RISING STARS AND FALLING ANGELS SUMMER 2010

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(4)

where ERCE is the certainty equivalent excess return andγ measures the degree of risk aversion. By replacing theexpectation with its sample estimate, the Morningstarrisk-adjusted return (MRAR) is obtained as

(5)

In its calculations, Morningstar uses γ = 2. As notedin Morningstar [2007], the functional form is chosenbecause “the model investor’s utility function is concave,and there is decreasing marginal utility as returns increase.It is steeper for negative returns and starts flattening outfor positive returns, and this puts more emphasis on down-side variation.”

When comparing the performance of several funds,it is important to consider differences in launch date,which can distort results because of external effects. Morn-ingstar incorporates age differences by calculating itsOverall Morningstar Rating as a weighted average of theseparate 3-, 5-, and 10-year ratings.3 This rule implies that,in order to be rated by Morningstar, a fund must have aminimum of 36 continuous months of total returns. Con-sequently, all funds rated have a 3-year rating, but onlysome receive a 5- or a 10-year rating.4

Within each age group, Morningstar sorts byMRAR. The breakpoints of the rating are (from top tobottom) 10%, 32.5%, 67.5%, and 90%. Thus, the top 10%receive a five-star rating, the next 22.5% receive a four-star rating, and so on. The overall rating is a weightedaverage of the available ratings. If a fund has only a 3-yearrating, the Morningstar rating would equal that of thethree-year. If both the 5- and 3-year ratings are available,the overall rating is calculated as 40% of the 3-year ratingand 60% of the 5-year rating. Finally, if all three ratings areavailable, the rating is calculated as 20% of the 3-yearrating, 30% of the 5-year, and 50% of the 10-year.

DATA AND METHODOLOGY

Our data come from Morningstar Inc. and includethe ratings of 2,490 funds, as well as their size and returns.Our sample consists of monthly data from May 2004

MRART

ERt tt

T

= +( )⎡

⎣⎢

⎤

⎦⎥ −

−

=

−

∑11 1

1

12

γ γ

1 1 1 01

+ = + > − ≠−−

E ER E ERCE( ) ( ( ) ) , ,γ γ

γ γthrough April 2009. The rating data come from theon-disk sample of April 2009 and consequently includeonly those funds available for sale during that month. Fur-thermore, delisted funds are not included, thus the data aresubject to survivorship bias.5

Most studies conducted on either performance pre-dictability or the flow effect of Morningstar ratings focuson equity funds investing solely in their home markets.However, this is not practical when analyzing the Germanmarket. Because of the lack of change within our sample,the use of an event study to analyze the effect of ratingchanges on fund flow would not be possible. Consequently,we include all fund classes and regions. We deleted fundswith missing entries for any variable, which reduced theactual sample to a total of 2,253 funds.

Methodology Used for the Dummy VariableRegressions

To determine the Morningstar rating’s forecastquality, we analyzed the average performance of an invest-ment in a given rating category. Following Blake andMorey [2000], we consider the star score of the funds forApril of every year during the sample period. We then cal-culate performance over each subsequent year, creatingfive non-overlapping observation windows. The threethree-year windows in the sample were also considered.

In order to examine post-rating performance, weapply a cross-sectional dummy variable regression. Thisenables us to identify performance differences within thestar groups. The dummy regressions are of the form

Si = C + γ4Di,4 + γ3Di,3 + γ2Di,2 + γ1Di,1 + μi (6)

where i = 1, …,N, N is the total number of funds in a sub-sample, Si is the performance over the observed subsampleof fund i as measured by the performance measure chosen(either the Sharpe ratio or the geometric mean), and D1through D4 are the dummy variables and refer to the starrating fund i received just prior to the evaluation period.

Here, the dummy variable Di,j is equal to 1 if fundi received a rating of J, and 0 otherwise. Variable µ is theregression error. The regression tests the hypothesis thatfunds with a given rating do not perform equally well.Thus, C is equal to the expected performance measure ifall the dummy variables are zero. Because this implies thatthe fund was not ranked with a one-, two-, three-, or

SUMMER 2010 THE JOURNAL OF WEALTH MANAGEMENT 77

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four-star rating, this can only be the case if the fund wasassigned five stars.

The group of five-star-rated funds hence forms areference group, and the performance of all other groupsis then measured in relationship to them. The differencein each group’s performance is expressed by γ1 through γ4.If funds are rated higher the better they perform, then γ1through γ4 should all be significant and negative, and theirvalues should be in the form that γ4 > γ3 > γ2 > γ1.

We next compare the performance measurementand predictability of Morningstar ratings to the Sharperatio and the geometric mean of the returns. In order tocreate these competing rankings, we calculate the Sharperatio and the geometric mean during the 36-month periodprior to each month the Morningstar rating was investi-gated. We choose a three-year estimation period becauseit has the highest influence on any Morningstar-ratedfund’s score and because data are available in full for allfunds.

We then rank the funds according to each calcu-lated performance measure and divide them into five sub-groups (in the same manner as Morningstar). Thistransforms the Sharpe ratio and mean returns into ordi-nally scaled variables. Consequently, we need to performa two-tailed Spearman rho rank correlation test to assessto what extent the Morningstar rating may calculate adifferent assessment of fund quality.

The Spearman rho test also tests the alternativehypothesis that the Morningstar rating is correlated withthe two performance measures. For the Morningstar rat-ings to achieve a ranking unique to that of the alterna-tive predictors, ρ should be either low or insignificant. Toassess how well the alternative performance measures pre-dict future fund quality, we perform the same dummyregressions as with the Morningstar ratings.

Methodology for Estimating Fund FlowInfluence

Because investors transfer their decision-makingpower to a fund manager when purchasing a fund, theonly way to express their opinion of the managementprocess is by investing or withdrawing their funds. Con-sequently, net fund flow has become the most prominentmeasure of investor reaction in the fund literature (DelGuercio and Tkac [2001]).

In line with these studies, we analyze the effect ofMorningstar ratings on net fund flow. First, following

Damato [1996], we examine on a cross-sectional basiswhether German investors allocate funds according toMorningstar suggestions. We then use an event studymethodology to measure how important such ratings actu-ally are to investors (see Del Guercio and Tkac [2001]and Eid et al. [2008]).

Cross-sectional analysis of fund flows. To analyzethe relationship between Morningstar ratings and fundflow, we calculate the average monthly net fund flow forfunds within the same star rating category. We define themeasure to be observed, net fund flow or the net amountof euro flows into or out of a fund i at time t, as:

Fit = log(TNAit –TNAit-1) (7)

where TNA denotes the total net asset value of fund i attime t. Net average fund flow should be of the form:

(8)

where is the average monthly net flow of euros into

one-star funds over the observation period, is the net

flow into two-star funds, is the net flow into three-

star funds, and and are average net euro flows into

four- and five-star funds, respectively. The data set includes

both a bull and a bear market. Hence, results are reported

for both the full sample and for two subsamples,

April 2004–December 2006 (the bull sample) and July

2007–April 2009 (the bear sample).We find a substantial difference between the sub-

samples. The average flow per month into all funds is€100.29 million for the bull sample, and the average out-flow per month is €237.61 million for the bear sample.

Event study analysis. The literature suggests ratingsare highly correlated with traditional performance mea-sures (Blake and Morey [2000]). However, the Morningstarratings can be distinguished from other measures in onesignificant way: They are not available on a continuousbasis, but are published once a month at a known time. Itis thus possible to identify specific points in time whenthe discrete star rating changes, while all other continuousmeasures do not.

Given this discrete feature, the econometricmethodology of an event study makes it an appealingway to extract the actual degree to which investors use

FS5FS4

FS3

FS2

FS1

F F F F FS S S S S1 2 3 4 5< < < <

78 FROM RISING STARS AND FALLING ANGELS SUMMER 2010

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Morningstar ratings. An event study can isolate the rating-specific flow from flows attributable to other influences(Gerrans [2006]).

The event study we use to test how much investorsperceive a Morningstar rating change as new informationrequires identifying and extrapolating usable rating changeevents. Out of the 141,180 data points (2,353 funds,60 months), we first identify those months in which afund’s rating at month t was not equal to that of montht – 1. We define these months, as well as the month priorand the month after, as the event window. We set the priorestimation window to 18 months and the observationperiod to 12 months.

Each event thus comprises a total of 33 data points.In order to prevent overlapping, we disregard events thatfall into the observation period of a prior event. We ulti-mately identify a total of 1,537 change events, as listed inExhibit 1. We excluded the two-step star changes becausethere were only 42 observations, which lowered theamount of change events to a total of 1,505.

Exhibit 1 shows the distribution of change eventsover rating classes. Note that the amount of up and downratings is fairly equal. The density is bell-shaped, with thesmaller amounts of changes toward one or five ratings,which is natural as their change potential is one-sided.

In order to correctly assess the effect of a ratingchange on fund flow, it is critical to set an adequate bench-mark of normal flow. If the estimation of expected flowreflects normal flows well, we can assume it will be ableto infer information from analyzing abnormal flows. Con-sequently, and following Del Guercio and Tkac [2001],we use the following factors to describe expected net

normal flow of a fund i during time t (E(Fi, t)): the netflow of fund i in t – 1 (Fi, t–1), the return of fund i in t – 1(Ri, t–1), and the average net flow of the Morningstar fundcategory the fund belongs to in time t ( ). We state thisin the form

(9)

We measure the abnormal flow of fund i in montht (AFi,t) as the difference between actual and expectedfund flow. Specifically,

(10)

where is the average abnormal flow to fund i calculated

during the estimation window, and , , and are thesensitivity factors of fund i to the lagged flow, lagged return,and average category flow, respectively, as calculated withinthe benchmark estimation (Del Guercio and Tkac [2001]).

To prevent funds with a high forecast variance fromdominating the statistical test, we standardize the funds bytheir estimated error variance of abnormal flow ( ). Thestandardized abnormal flow of a fund i (SAFi) is hence

(11)

We then group these standardized abnormal flowswith regard to the fund’s rating prior to and after its ratingchange. We also calculate their averages. Consequently,for every month and for each type of transition, we cal-culate the average standardized abnormal flow scores inthe form that

(12)

where ASAFS,t is the average standardized abnormalflow within a given star group (S) at time t, is thestandardized abnormal flow of every single fund i at timet belonging to that group, and n is the number of fundsin the group. This is in line with Del Guercio andTkac’s [2001] calculations, which will enable us to makea comparison.6

FC t,

E F F R Fi t i i i t i i t i C t( ), , , , ,= + + + +,1 − ,2 −α β β β ε1 1 3 ii t,

SAFi tS,

SAFAF

i ti t

i

,,

ˆ=δ

ASAFSAF

nS t

i tS

i

n

,

,= =∑ 1

δ̂i

ˆ,β3 i

ˆ,β2 i

ˆ,β1 i

α̂ i

AF F F Fi t i t i i i t i i t, , , , , ,( ˆ ˆ ˆ ˆ= − + + +− −α β β β1 1 2 1 3,, ,

, ,

)

( ˆ )

i C t

i t i t

F

F E F= −

SUMMER 2010 THE JOURNAL OF WEALTH MANAGEMENT 79

E X H I B I T 1Descriptive Statistics of Change Events

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When defining the length of the observation window,there is a trade-off between choosing enough observationpoints to incorporate all potential abnormal flow andselecting the minimum necessary. We choose a 12-monthwindow, from month t + 2 to t + 13, to observe whetherMorningstar rating changes trigger abnormal flow.

This time period is longer than that of Eid et al.[2008] or Del Guercio and Tkac [2001], who use three-and six-month windows, respectively. We choose theextended observation window because very short periodsmight only capture the reactions of investors actively fol-lowing the Morningstar publication. Shorter periods mayalso neglect abnormal fund flow in later months causedby investors who reallocate their portfolios less frequently.Additionally, the literature notes a strong usage of Morn-ingstar ratings in fund advertising (Jain and Wu [2000]).Thus, we may observe a lagged abnormal response that isattributable to marketing efforts.7

RESULTS

Exhibit 2 gives the results for the dummy variableregression using the Sharpe ratio as a benchmark. Theboldface numbers represent values that are significant atthe 5% level (p-value < 0.05).

Note that some annual observations exhibit a sig-nificant intercept. While the two intercepts estimatedbetween May 2004 and April 2006 are positive, those esti-mated after April 2007 are negative.

The values of (column D4) are all insignifi-cant for all time horizons observed, which indicatesthere is no significant performance difference betweenfour- and five-star-rated funds. We also see that only 6of the 15 calculated gammas for medium-low-ratedfunds are significant, while one-third of the calculatedcoefficients for D4 to D2 are positive. This is exactlythe opposite of the expected negative relationship.

Furthermore, the dummy regression seems to indi-cate that the performance predictability, while not goodduring up-markets, is even lower in down-markets. Duringthe down-market period (regressions 05/2007–04/2008,05/2008–04/2009, and 05/2006–04/2009), the coeffi-cients appear randomly distributed across both positiveand negative values. Additionally, only 3 of the 15 coef-ficients in these regressions are significant.

However, there seems to be some support for thetheory that five-star-rated funds outperform one-star-

γ̂ 4

rated funds. In three of the time periods, the D1 coeffi-cient is significantly negative, so we can reject the nullhypothesis of a zero coefficient in roughly two-thirds ofthe observed time periods. Thus, while the Morningstarrating does not seem able to distinguish high-performingfunds from mediocre funds, it does seem to have limitedability to identify very low performing funds.

When analyzing the relevance and significance ofcoefficients estimated by the Morningstar rating withregard to mean monthly return, we see that a similar pic-ture emerges. These findings are similar to those of Blakeand Morey [2000], who also report a low amount of sig-nificant coefficients for three- and four-star funds and ahigher amount of significant and negative one-stargammas. They also note a higher number of significant andnegative two-star gammas, however, and they find thatMorningstar ratings might have some limited ability toidentify one- and two-star funds. We did not find two-star gammas to be significant on the German market.

The R2 values are the most notable data within theregression analysis. Less than 3% of the variance of thepost-rating performance can be explained by the inde-pendent variables.8 Such low values are roughly in linewith those of Morey and Gottesman [2006], who find a0.0366 R2, on the lower end of those calculated by Blakeand Morey [2000], who found R2 values ranging from0.02 to 0.17.

Exhibits A1–A5 in Appendix A give the regressionresults for the two benchmark predictors. The explana-tory power of the mean monthly return measure rangesfrom an R2 of 0.02 to 0.51 (Exhibits A3 and A5); theexplanatory power of the Sharpe predictor ranges from0.08 to 0.59 (Exhibits A2 and A4). These values are muchhigher than the R2 values calculated for the Morningstarregression (Exhibits 2 and A1).

We next conduct a Spearman rho rank correlationtest in order to examine whether the rating measures arealike. Exhibit 3 gives the results. All rankings are positivelycorrelated at a 1% significance level. We must reject thenull hypothesis of no correlation. The ρ values range from0.13 to 0.44. Consequently, while the rating values arecorrelated with other measures, they are not fully equal.This justifies a further comparison of the performancepredictability of the three ratings.

80 FROM RISING STARS AND FALLING ANGELS SUMMER 2010

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Fund Flow Influence: Cross-Sectional Results

Our aim in performing a cross-sectional analysis wasto determine the importance of Morningstar ratings byanalyzing to what extent they concur with the invest-ment choices of German investors. The relationshipbetween average fund flows and ratings are in Exhibit 4.For the sake of clarity, the data is also provided as a graphin Exhibit 5.

We see that the relationship of the average netmonthly flows is not in line with the expectations of a pos-itive flow rating relationship (Del Guercio and Tkac [2001,Figure 1]). Although one-, two-, and four-star rating cate-gories achieved on average positive net monthly flows,we can infer that three- and five-star-rated funds actuallysuffered net monthly outflows. The net outflow of three-star-rated funds is close to zero and hence does not deviate

greatly from that of one- and two-star-rated funds. But the net monthly outflowfor five-star funds is €14.48 million, a dra-matic difference from the €24.13 millioninflows for four-star funds.

For the full sample observationwindow, the expected relationship of

clearly doesF F F F FS S S S S1 2 3 4 5< < < <

SUMMER 2010 THE JOURNAL OF WEALTH MANAGEMENT 81

E X H I B I T 2Dummy Variable Regression Results of Morningstar Rating Tested by Sharpe Ratio

Measurement Period Constant0.279

(–4.902)0.161

(1.268)0.159

(3.716)–0.309

(–1.313)–0.557

(–1.820)0.226

(5.038)0.278

(6.432)–0.300

(–2.276)

D4–0.053

(–0.799)0.014

(0.956)–0.083

(–1.660)–0.023

(–0.859)0.022

(0.626)–0.053

(–1.010)–0.071

(–1.401)0.003

(0.191)

D3–0.092

(–1.217)0.031

(2.226)–0.184

(–3.858)–0.058

(–2.286)0.071

(2.154)–0.156

(–3.176)–0.197

(–4.175)0.003

(0.182)

D2–0.123

(–1.882)–0.001

(–0.063)–0.429

(–6.930)–0.047

(–1.745)0.028

(0.795)–0.047

(–0.914)–0.126

(–2.551)0.004

(0.282)

D1–0.156

(–2.485)0.001

(0.067)–0.236

(–4.640)–0.032

(–0.923)0.009

(0.209)–0.016

(–0.267)–0.131

(–2.306)–0.031

(–1.615)

R2

0.020

0.007

0.027

0.004

0.005

0.013

0.011

0.002

F-Stat2.460

3.850

1.652

2.184

2.682

6.449

6.628

1.391

May/2004–April/2005

May/2005–April/2006

May/2006–April/2007

May/2007–April/2008

May/2008–April/2009

May/2004–April/2007

May/2005–April/2008

May/2006–April/2009

1-YearHorizon

3-YearHorizon

Notes: t-Statistics in parentheses. Coefficients in bold are significant at the 5% level.

E X H I B I T 3Results of the Spearman Rho Rank Correlation Test

Notes: t-Statistics in parentheses. Coefficients in bold are significant at the5% level.

E X H I B I T 4Monthly Average Flows into Morningstar Star Categories (in € m)

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not hold. However, including bull and bear market datamight result in a stronger influence of behavioral biases.So we provide the results for both separate subsamples.

When comparing the subsample results, the differ-ence in investor attitude becomes clear. The flow ratingrelationship is close to that found in the literature duringthe bull market period (except for a slightly high netaverage flow for two-star funds, which had a 0.75 millionhigher average inflow than three-star funds).

However, this relationship is nearly reversed for thebear market period, when all funds suffered outflows.Five-star-rated funds suffered an average net outflow of€148.43 million. This is almost five times the outflowsuffered by four-star funds, and 1.6 times the dollar out-flow of all other fund star categories combined. Thus, wefind that five-star-rated funds profited the most duringthe bull market period, but they also suffered the greatestlosses during the bear market.

This volatility is especially remarkable comparedto four-star-rated funds, which managed to achieve a€24.13 million inflow over the total sample period. Thismay suggest that five-star funds tend to attract investorswho will flock into highly rated funds during upward-trending markets but who will divest their holdings in

falling markets regardless of perceived fund quality. Thisinvestment strategy is in line with the behavioral biastheories of herding and momentum trading.

However, another interpretation is that investors areattempting to realize investment gains. Thus, after a fundhas performed well (and thereby achieved a five-starrating), investors are likely to withdraw their funds. On theother hand, investors may remain in poorly performingfunds in the hope of realizing future gains. This result isin line with Jacobs and Levy [1996], who argue that activefund investors are not risk-averse in the common sense,but rather regret-averse.

Fund Flow Influence: Event Study

The aim of our event study analysis was to investi-gate whether investors react to a change in Morningstarratings by changing their allocation attitude. The nullhypothesis is that the abnormal flow for funds with arating change is zero. Exhibit 6 gives the average stan-dardized abnormal flow (ASAF) results for rating down-grades during the 12 time periods of the observationwindow.

82 FROM RISING STARS AND FALLING ANGELS SUMMER 2010

E X H I B I T 5Average Fund Flows of Morningstar Ratings

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The results clearly show a large amount of negativeASAFs. Only 11 of the 48 abnormal flows are positive, andtherefore contrary to the expected effect of downgradingon fund flows. If we include the sum values, only 12 ofthe ASAF scores are positive. Of these 8 are in the cate-gory of downgrades from five to four stars, indicating thata downgrade to four stars is not punished as strongly byinvestors. Exhibit 7 depicts the ASAF for downgrades overthe observation period.

However, note that the overall significance of theseabnormal flows is low. The rating downgrade from twoto one stars does not result in any significant abnormal neg-ative outflows. A downgrade from four to three stars resultsin only one month of significant outflows, while a down-grade from five to four stars results in just one significantabnormal flow, which was positive.

Yet, in contrast to the other downgrades, the three-to two-star downgrade seems to have a clear significantly

negative effect on fund flow. Of the 10 significant monthlyabnormal flows, 8 are attributable to a two-star down-rating. Note also that the error probability decreases forlater months. For example, in months 9 to 12, we canreject at the 5% level the null hypothesis that a down-rating to two stars leads to an abnormal return of zero. Forperiods 1 and 2, we cannot reject the null hypothesis ofno abnormal flow.

Comparing these results to findings in the U.S.market, we see that the German market generally exhibitsa less severe reaction to Morningstar downgrades. DelGuercio and Tkac [2001], who only examine sevenmonths’ of data after the rating change, find that 75% ofmonths exhibit abnormal flow. In fact, if we adapt to theirtime horizon and significance level by only looking at thefirst seven months of our data, we find that only onemonth shows significant flows at their 5% boundary.

Such an acute contrast suggests that Germaninvestors are less and later influenced by Morningstar

SUMMER 2010 THE JOURNAL OF WEALTH MANAGEMENT 83

E X H I B I T 6Average Standardized Abnormal Flow for Rating Downgrades

Notes: t-Statistics in parentheses. *denotes significance at the 10% level, **denotes significance at the 5% level, and ***denotes significance at the 1% level.

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rating changes. However, Del Guercio and Tkac [2001]also do not find that down-ratings to four stars spur neg-ative flows. They find rating upgrades lead to positiveabnormal inflows in all months over all rating changes, butonly 29 of the 48 months analyzed here show positiveabnormal flows (see Exhibit 8). Exhibit 9 plots the ASAFsfor rating upgrades.

Again, the number of significant fund flows is sub-stantially lower than in the U.S. For the one- to two-starand the two- to three-star categories, we find only onetime period each is significant at the 10% level, and bothdisplay negative abnormal flows.

However, the calculated ASAFs exhibit someabnormal flow for changes in higher rating categories.For example, a change from three to four stars leads to sig-nificant abnormal flow over the first three periods. Achange from four to five stars leads to significant inflowin periods 1 and 2 and to another three months of posi-tive abnormal inflow in months 6, 7, and 8. These find-ings suggest that German investors perceive high ratingsas new information and act accordingly by shifting moremoney into funds after they receive a five-star rating.

When comparing investor response toward upgradesand downgrades, it is clear that upgrades seem to affectsignificant abnormal flows rather immediately, while theresponse is lagged for downgrades. This is consistent withcross-sectional studies and the theory of a relationshipbetween convex fund flow and past performance.

And this asymmetry, that investors react to upgradesat the upper end but only react to downgrades at the lowerend, suggests that investors “chase” high-performing fundsbut are averse to reversing allocation decisions. This resultis again in line with regret aversion. Thus, investors donot adjust their holdings directly, because they are averseto the regret of having chosen an underperforming fund.

We posit three potential reasons for the differencesbetween the German and U.S. studies on the influence ofratings. First, German investors may conform more to thetheories of modern portfolio management and the effi-cient markets hypothesis (EMH). They may not perceivethat ratings, primarily a measure of past performance, arepredictive of future results.

Second, the data or model used could bias the results.Given that the star-changing event data were taken froma sample that included bull and bear markets, and the

84 FROM RISING STARS AND FALLING ANGELS SUMMER 2010

E X H I B I T 7Abnormal Standardized Average Flow during Observation Period for Morningstar Downgrade Events

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strong difference in investor behavior within these twophases, it is possible that results of a cross-category com-parison would cancel each other out. This may have dis-torted the results. Additionally, a test for finding abnormalflow would depend on the quality of the model used tocalculate the benchmark flows. Because determiningabnormal flow is always dependent on how well normalflows were forecasted, a lower-quality benchmark couldalso distort the results.

Third, German fund investors might give lessweight to ratings when making investment decisions.Considering the strong ties between German investorsand their house banks, as well as the closed-architecturedistribution system inherent in the German fundindustry, it is quite possible that German investors donot feel as liberated as their U.S. peers and do not use

independent research as vigorously. It is then possiblethat the significance found in five-star ratings is not dueto investors actively searching for Morningstar five-star-rated funds, but to stronger marketing efforts on the partof banks (Jain and Wu [2000]). This might also explainthe partially lagged reaction to five-star upgrades.

CONCLUSION

Despite the large body of literature on the predictiveabilities of ratings with regard to future fund performanceand the influence ratings have on investors, we found a gapin these issues with regard to the German market. Thisarticle aimed to provide a first step toward narrowing thisgap. We investigated the two fundamental questions of per-formance predictability and investor reaction by using adata set of Morningstar ratings from May 2004 throughApril 2009.

The results of a performance predictability analysissuggest that the ability of the Morningstar rating to pre-dict future fund quality is limited for the German fundmarket. We could not reject the null hypothesis of no per-formance differences among five-, three-, four-, and two-star ratings in the majority of observation periods. Thissuggests that the Morningstar rating cannot separatehigher-performing funds from medium-performing funds.

However, we found some limited evidence that therating can identify funds that will perform very poorly inthe future, as the one-star category performed worse thanthe five-star category in more than half the observationperiods. This quality difference held only during up-market times, however; we found performance pre-dictability to be virtually negligible during down-markets.

Note also that we performed a Spearman rho rankcorrelation test, which indicated that the Morningstarrating was significantly correlated with a three-year Sharperatio and a three-year mean monthly return rating, butthe correlation was less than 0.5. We believe the Sharperatio might be a better forecasting tool than the Morn-ingstar rating, because it was able to somewhat differen-tiate between five- and one-star groups, while alsoidentifying two-star funds to a certain extent. However,our overall results suggest that neither performance pre-dictor is a totally solid forecasting tool.

We also conducted a cross-sectional analysis to deter-mine whether investors allocate their funds in line withthe quality suggestions of the Morningstar rating. Wefound that German investors, unlike their American peers,

SUMMER 2010 THE JOURNAL OF WEALTH MANAGEMENT 85

E X H I B I T 8Average Standardized Abnormal Flow for RatingUpgrades

Notes: t-Statistics in parentheses. *denotes significance at the 10% level,**denotes significance at the 5% level, and ***denotes significance at the1% level.

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do not. Five- and three-star funds suffered average netmonthly outflows, while all other fund groups experi-enced inflows.

We further investigated this relationship by subdi-viding the sample into an up- and a down-marketperiod. This revealed that the German fund flow per-formance relationship seems to be positive and convex,and hence in line with the international literature duringup-markets. However, the relationship was inverse duringbear markets. Thus, highly rated funds suffered muchgreater outflows than lower-rated funds.

Our other primary aim here was to examine investorreaction to Morningstar rating changes. We used the eventstudy methodology to analyze net fund flows. Our resultssuggest that German investors are less affected by ratingchanges than U.S. investors. For rating downgrades, onlya three-star-to-two-star change incurred a larger numberof months with significant outflows. Additionally, we foundthat German investors seem to react more slowly thanAmerican investors, as significant outflows from this down-grade did not appear until three months after the ratingchange.

For rating upgrades, we found a more immediateinvestor response for upgrades to four or five stars. The

significant inflows due to lower upgrades were marginal.As for the downgrades, however, results showed suffi-ciently fewer months of significant outflow than U.S.studies, which again suggests that German investors maybe less influenced by Morningstar ratings in their allo-cation decisions.

Potential further research is vast, particularly giventhe lack of attention to the German mutual fund marketthus far. Two interesting aspects deserve further attention.First, the sharp reversion of the fund flow rating rela-tionship during the down-market was quite contrary toresults of preceding studies of investor reaction. This sug-gests that we could glean further important insight intohow investors truly use ratings from conducting moreresearch on flow rating behavior during financial crisesor down markets.

Second, the event study methodology seemed tosuggest that investors tend to show a lagged reaction tooutflows and an immediate attention to inflows. How-ever, a lagged significant abnormal inflow was observedfor five-star-rated funds. This might suggest that fundcompanies are advertising the high rating of five-star funds.If this is the case, it might be interesting to attempt to dis-entangle the true effect of a Morningstar upgrade fromthe impact of the advertising itself.

86 FROM RISING STARS AND FALLING ANGELS SUMMER 2010

E X H I B I T 9Abnormal Standardized Average Flow during Observation Period for Morningstar Upgrade Events

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A P P E N D I X A

SUMMER 2010 THE JOURNAL OF WEALTH MANAGEMENT 87

E X H I B I T A 1Dummy Variable Regression Results of Morningstar Rating Tested by Mean Monthly Return

Notes: t-Statistics in parentheses. Coefficients in bold are significant at the 5% level.

E X H I B I T A 2Dummy Variable Regression Results of Sharpe Ratio Rating Tested by Sharpe Ratio

Notes: t-Statistics in parentheses. Coefficients in bold are significant at the 5% level.

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88 FROM RISING STARS AND FALLING ANGELS SUMMER 2010

E X H I B I T A 3Dummy Variable Regression Results of Mean Return Rating Tested by Sharpe Ratio

Notes: t-Statistics in parentheses. Coefficients in bold are significant at the 5% level.

E X H I B I T A 4Dummy Variable Regression Results of Sharpe Ratio Rating Tested by Mean Return

Notes: t-Statistics in parentheses. Coefficients in bold are significant at the 5% level.

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ENDNOTES

1Neither the press release announcing this rating change(Morningstar [2002]) nor the fact sheet describing the mostimportant changes mention this load-adjusting innovation.

2Blake and Morey [2000] and Blume [1998] adjust forloads; private investor advisories warn to pre-consider ratings.

3Morningstar uses the term “Overall Morningstar Rating”when directly comparing its weighted measure with the 3-, 5-,and 10-year measures. However, the Overall Morningstar Ratingis usually referred to simply as the Morningstar rating. We usethese two terms interchangeably.

4Sharpe [1997] finds that only 27.8% of U.S. domesticequity funds receive a 5-year or a 5- and 10-year rating.

5The quality of raw data available to analyze Morningstar-rated funds in the U.S. has risen significantly since the intro-duction of the CSRP’s Survivor-Bias-Free Mutual FundDatabase, which offers data for all funds terminated and trading.Unfortunately, such a survivorship-free data sample does notyet exist for funds traded on the German market. Future researchmay examine the influence of survivorship bias within theGerman market data, but it is beyond the scope of this article.

6Del Guercio and Tkac [2002] are not consistent inexplaining how they calculate their standardized measure“ASTAF.” They cite both the variance of abnormal flow andthe variance of normal flow as their “standardizer.” Given thescale of their derived values, it is reasonable to assume they usedabnormal flow; hence we also used it (see Del Guercio andTkac [2002, Table 4, Panel A]).

7Note that abnormal flow due to advertising should onlylead to abnormal flow for upgraded funds, as fund managementcompanies are unlikely to market downgraded funds.

8This number is even lower for the Morningstar dummyregression conducted on the mean return measure, where nomodel can explain more than 1% of the variance inherent inthe dependent variable.

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