Is there still an Age Bias in the Morningstar Ratings?
by J. A. Adkisson and Don R. Fraser
Both J. A. Adkisson and D. R. Fraser are members of the Department of Finance, Mays Business School, Texas A&M University, College Station Texas, 77845. Telephone: 979.845.3541. Please address all correspondence to J. A. Adkisson,
[email protected]. Please do not quote without permission of the authors.
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Is there still an Age Bias in the Morningstar Ratings?
ABSTRACT: We investigate three sources of age bias in the Morningstar mutual fund rating system: Morningstar’s return weighting system, market conditions and cycles over the evaluation period, and fund size. We find that Morningstar’s funds rating methodology introduces an age bias into the star ratings that has two fundamental properties. An inverse relationship exists between fund age and the star ratings which makes it difficult for older funds to earn top marks. Furthermore, the return weighting algorithm also creates a tendency toward convergence within the ratings, a property that is exacerbated by the effect of fund size. As fund size increases with age, older funds become progressively less likely to exhibit extreme performance, especially if their more recent performances were good. We also provide evidence that the age bias persists as the market climate shifts from bull market to bear market conditions.
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Is there still an Age Bias in the Morningstar Ratings?
I. Introduction. The potential for age bias in the Morningstar ratings is significant because of the widespread use of the star ratings as a fund selection tool1. If the ratings contain an age bias, this will limit the comparisons and conclusions that can be made by examining the number of stars assigned to individual funds. Blume (1998) was among the first to investigate the fund age/star rating relationship. In the process of tallying up the star ratings of funds by fund age group, he found that fewer older funds received the top rating of five stars. Morey (2002) subsequently used regression techniques to analyze the Morningstar ratings and concluded that the overall star ratings of older funds are higher than younger funds. He attributed this finding to Morningstar’s return weighting system. More recently, Adkisson and Fraser (2003) have proposed two additional sources of age bias: the market conditions and cycles over the evaluation period and the size or total net assets of the fund. Three things immediately stand out from this brief review of the literature. First, while Blume and Morey both find an age bias in the Morningstar ratings, they differ as to its nature. Second, they examined the star ratings in the period before Morningstar revised its methodology2. Therefore, the relevance of their findings to the new rating system is unknown. Finally, only the issue of the return weighting system has been 1
For example, Del Guercio and Tkac (2001) and Adkisson and Fraser (2003) show the influence of the star ratings in determining the flow of new investment dollars into mutual funds.
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seriously probed. How market conditions and fund size may create or contribute to an age bias has yet to be determined. We examine all three potential sources of age bias in the Morningstar ratings and foster a deeper understanding of the age bias by identifying its fundamental properties. Because we use data from the June 2002 Principia disk, our tests and conclusions are based on Morningstar’s current fund rating methodology. The paper is organized as follows: In Section II, we introduce the fund rating methodology and provide an in-depth examination of the Morningstar return weighting algorithm, demonstrating the influence of a fund’s most recent 36 monthly returns on its overall star rating. In Section III, we discuss the ways in which market conditions and cycles can bias the Morningstar ratings. While the fund rating methodology has been changed to make it less sensitive to the revolving popularity of various investing styles and market sectors, market conditions and cycles continue to affect the ratings by inducing volatility in equity risk premiums. Section IV discusses how fund size contributes to the age bias by making it difficult for large funds to exhibit extreme performance. Section V contains a description of our data set. In section VI, we provide a series of regression models that clearly illustrate two fundamental properties of the age bias in the Morningstar ratings: 1) an inverse relationship between a fund’s age and its rating and 2) a tendency for funds’ ratings to converge. To establish the inverse relationship, we look at how a fund’s overall star rating is affected by its age for funds of all ratings and age groups. Then, we examine the time-specific ratings to see if they contain a similar bias. To test for convergence, we
2
Star ratings based on the new methodology first became publicly available in June, 2002.
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examine the pattern of overall star ratings with respect to age, while holding the threeyear time-specific rating constant. In all of our tests, we adjust for fund size, managerial tenure, and Jensen’s alpha, providing a much clearer picture of the true relationship between a fund’s age and its overall Morningstar rating. Our conclusions are presented in Section VII. Our regression tests demonstrate the two properties of the age bias. The initial tests reveal an inverse relationship between a fund’s age and its star ratings. Older funds are significantly less likely to receive the highest ratings, both with respect to the overall ratings and the time-specific ratings. Additional tests show the tendency toward convergence. The oldest funds get higher overall ratings when their three-year ratings are low, and lower overall ratings when their three-year ratings are high. An underlying relationship between fund age and size reinforces the convergence property. As funds become older, they usually become larger, too, and large funds are less likely to exhibit extreme performance, either positive or negative. Tests that control for fund size, however, show that a fundamental age bias remains which can be directly attributed to Morningstar’s return weighting algorithm. Unfortunately, the effects of market climate and conditions over the performance evaluation period are not as easily partitioned because this would amount to controlling the star ratings for systematic risk. If systematic volatility causes equity risk premiums to vary over time, fund ratings will not be directly comparable unless the dates and lengths of the measurement periods are identical.
II. Morningstar’s return weighting system
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Morningstar’s rating system is based on risk-adjusted returns3. Morningstar ranks each fund on this performance measure, excluding funds with less than three years of data. Funds in the top 10% of their group receive the highest rating of five stars. The next 22% get four stars and the 35% ranking in the middle of their group get three stars. Finally, the next 22% receive two stars and funds in the lowest 10% of their group receive the lowest rating of one star. For funds old enough to have sufficient data, this process is repeated using the most recent five and ten years of fund performance data, resulting in what is commonly referred to in the literature as a fund’s “time-specific ratings”. Thus, “young” funds have only a three-year time-specific rating, “middle-aged” funds have three- and five-year time-specific ratings, and “seasoned funds” have three-, five-, and ten-year time-specific ratings. Morningstar weights and combines each fund’s individual time-specific ratings to form the overall star rating, the rating most commonly advertised4. Morningstar’s weighting system places the greatest emphasis on a fund’s most recent thirty-six monthly returns. Let R36, k represent a vector of the most recent 36 monthly returns for fund k for the past three years. Following Blume, the three-year cumulative percentage return for fund k can be defined as:
Rk = {
36
[ ∏ ( 1 + rkt ) ] − 1 } 100 t =1
3
Bagnoli and Watts (2000) demonstrate the importance of using risk-adjusted returns in making mutual fund portfolio decisions. 4 Advertising a high rating may help funds attract new investment dollars, as shown by Jain and Wu (2000). This would increase the profitability of the fund to its managers.
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The rkt are the serial returns from the vector Rk, 36. Since we use data from the June 2002 Principia disk, this vector consists of monthly fund returns from June 2002, stretching back to June 1999. This vector is the basis for each fund’s three-year time specific star rating. Similarly, a vector Rk, 60, representing the most recent monthly returns for the past five years, and a vector Rk, 120, which consists of the most recent 120 monthly returns, form the foundation of a fund’s five- and ten-year time-specific rating. Because the measurement periods overlap, when the time-specific ratings are weighted and summed to form the overall star rating for each fund, the influence of Rk, 36 is magnified. The funds’ 36 most recent monthly returns are not only the sole basis of its three-year rating, but also a significant component of both its five-year rating and its tenyear rating, assuming the fund has been in existence long enough to receive all three time specific ratings. Consider the case of the seasoned funds and let f(Rk,36 ) represent fund k’s risk adjusted returns. The vector Rk, 36 forms 100% of the three-year rating, 60% of the five-year rating, and 30% of the ten-year time specific rating5. To calculate the overall star rating of seasoned funds, Morningstar assigns a weight of 20% to the three-year time specific rating, 30% to the five-year time specific rating, and 50% to the ten-year rating. Therefore, because
0verall rating = 0.2 (3-year time specific rating) + 0.3 (5-year time specific rating) + 0.5 (10-year time specific rating)
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For example, the vector Rk,36 contains 3/5s of the total number of returns in the 60 month evaluation period, so the weight of Rk,36 in the five-year time specific rating is 60%. Similarly, thirty percent of the 10year rating is based on the Rk,36 vector.
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it follows that
Total weight of Rk,36 =0.2 [1.00* (Rk,36 )] + 0.3 [0.60* (Rk,36 )] + 0.5 [0.30* (Rk,36 )] = 0.53 [(Rk,36 )]
By the same logic, the weight of the Rk,36 return vector in the overall star rating for a middle-aged fund is 0.76. The weight of the most recent 36 monthly returns is 100% in the overall star rating of young funds. Sharpe (1997) observed that a fund’s overall rating is usually either equal to the three-year time specific rating, or within one star of it. Clearly, this is a natural consequence of Morningstar’s return weighting system, which places the most emphasis on a fund’s most recent 36 monthly returns.
III. How Market Conditions and Cycles Bias the Star Ratings
The old ratings were sensitive to the popularity of investment fashions or styles, such as growth or value investing. In the words of a Morningstar representative: “When a particular style of investing was hot …a disproportionate share of funds within that style received 4 or 5 stars. It didn’t matter if the manager was good or bad. By being in the
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right place at the right time, they were able to pick up stars.”6 The investment style issue is even more important when the popular style or sector is highly volatile. In the late 1990s, for example, growth and technology funds frequently received high star ratings because the old rating methodology rewarded their huge returns without adequately recognizing the great risks some of these funds were taking. To make its fund rating system less vulnerable to the changing popularity of investment styles, Morningstar expanded the number of fund categories from four to forty-eight, placing each fund in a much more narrowly defined peer group. In the past, for example, all domestic equity funds were lumped into one group. Now, a large-cap growth fund’s performance is ranked solely against that of other large-cap growth funds, an “apples to apples” comparison. Morningstar also adjusted its risk measure to make it more difficult for funds to garner more stars by rotating into trendy but volatile market sectors. While maintaining a traditional emphasis on downside risk, the new methodology more fully recognizes upside volatility. As a result, consistent performance is more highly rewarded and it is more difficult for highly positive short-term returns to mask the inherent risks of a fund. While these changes certainly improve the fund rating system, they do not completely eliminate the influence of overall market conditions and cycles because these factors encompass more than just fashionable investment styles and hot market sectors. Major market cycles and shifting market conditions create volatility in equity risk
6
Quote from Christine Benz, “Introducing Morningstar’s New Star Rating”, dated July 3, 2002. Article on www.Morningstar.com
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premiums7. Over time, variability in the risk premium may change the relative rankings of funds by raising and lowering the average performance of the benchmark groups – but not necessarily at the same moment or by the same amount. In our data set, for example, the star ratings of the young funds are based firmly on returns drawn from a bear market. However, slightly more than half of the returns that make up the overall ratings of our middle-aged funds were earned under much more favorable business conditions. Thus, the changing state of the market (and the business cycles it reflects) may affect the apparent relationship between the age of a fund and its overall star rating. If the equity risk premium varies as we move from a bull to a bear market cycle, then the star ratings of funds of different ages may be disproportionately affected and the relative rankings of funds, even within the improved reference groups, may change.
IV. How Fund Size Contributes to the Age Bias
Fund size contributes to the age bias in the Morningstar ratings. Because compensation to the fund management company is typically a percentage of assets under management, and because there are some economies of scale that make larger funds less expensive to run, there is a strong incentive to increase fund size as quickly as possible8. The best way for a fund to grow larger is by investing in appreciating securities. When the fund’s portfolio is small, a few big winning positions can make a noticeable 7
See Arnott and Bernstein (2002). Wermers (2000) suggests that economies of scale may actually benefit the fund’s management. A decline in trading or other costs gives the fund room to increase management fees without changing the total cost to 8
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difference in the fund’s total return. This increases the fund’s ability to compete for new investment dollars and causes the capital gains tax to kick in and discourage investors from selling. As a result, as funds grow older, they tend to become larger. And because larger funds usually have a greater number of holdings, they begin to resemble the overall market as they age. The larger number of holdings makes it more difficult for any one position, or even any subset of positions, to greatly alter the course of the entire portfolio. This size effect will make it more difficult for the seasoned funds to produce extreme performance, and thereby to earn either the highest or the lowest rating. In other words, the larger size of seasoned funds will cause their star ratings to be more tightly grouped and to exhibit less variation9.
V. The Sample Data
Our sample is drawn from the set of all domestic equity funds represented in the Morningstar Principia data disk as of June 2002 and includes only the funds that can be classified into one of the nine cells of the Style Box ™. This allows us to implicitly control for the effect of style orientation (growth, value, or blend and small, medium or large market cap) on fund ratings. Funds that are too young to have received a three-year time specific rating are eliminated. We also exclude a very small number of funds with reported expense ratios in excess of 10% because these atypical funds may act as outliers
investors. The shift in cost structure increases the fund’s profitability to its managers without perturbing investors. 9 Morey (2003) and Blake and Morey (2000) show that high Morningstar ratings often do not predict favorable future performance because funds typically have trouble maintaining strong initial results. Increasing fund size no doubt exacerbates this difficulty.
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and distort the overall results of our tests. This leaves us with a sample of 3754 funds. The stratification of this sample by age and overall star rating is provided in Table 1. Table 1 gives an initial impression of the relationship between a mutual fund’s age and its overall star rating. The cells of Panel A show the number of funds of each age that received each of the five possible overall star ratings. For example, 118 young funds received five stars, the highest possible overall rating, and 108 received the lowest rating of one star. The column totals show the age distribution of the sample. Unlike prior studies, most of our funds (1736) are middle-aged. The row totals in Panel A reveal that most of the funds in our sample (1340) are three star funds. Only 309 of the sample funds merited five stars, regardless of the age of the fund. The cells of Panel B, Table 1 also provide the stratification of the sample data by overall star rating and fund age. The cells give the percentages of sample funds in each group. The statistics shown in this panel suggest that a seasoned fund is less likely to attain the highest rating than a young fund. About 9% of the young funds received five stars, but only about 6.6% of the oldest funds earned this overall rating. This is an interesting result because our data include a much high proportion of bear market returns. Seasoned funds are also less likely to receive a one-star rating. Only 7.69% of the seasoned funds earned the lowest rating, but 8.46% of the young funds fell into this category. It appears that the bottom of the distribution is trimmed as funds age, most likely through the elimination of poor performers. Table 2 illustrates the fund age/rating/size interaction. This table was constructed from exactly the same raw data as Table 1, but instead of showing the numbers and
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percentages of funds by age and overall star rating, it shows the total dollar value of funds in each category. The column totals show that the seasoned funds are much larger than the others. The total dollar value of seasoned funds ($1.4 trillion) is more than twice as great at that of middle-aged and young funds, combined ($658 billion). The oldest and largest funds are not, however, usually five-star funds. The row totals show that threeand four-star funds (regardless of age) account for about 70% of the total value invested in all funds. Moreover, the greatest dollar value is found in three- and four-star seasoned funds. These funds account for about half of the total dollar value of all of the mutual funds in our sample. Since the total number of seasoned three- and four-star funds is 458 (see Panel A, Table 1), this means that almost 50% of the total value resides in only 12% of the funds. These numbers suggests that the average size of a fund in these two groups, combined, is about $22 billion. To provide further evidence that it is difficult for large seasoned funds to exhibit the extreme performance needed to earn either the highest or lowest star ratings, we split our sample into indexed and actively managed funds, without regard to fund age. We found that no index funds in our sample are five-star funds and none are one-star funds. Few are two-star funds. Most are three- and four-star funds. There are more four-star than two-star index funds because the lower expenses of these funds lead to higher star ratings. Therefore, a group of funds that should, in theory, receive average ratings, does, in fact, get average ratings, with a skew in the upward direction that is easily explained by the more favorable cost structure of index funds. Since most of the index funds are also large, middle-aged or seasoned funds, this finding is consistent with a fundamental
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cross-sectional inverse relationship between fund ratings and fund age, overlaid with a tendency for larger funds to dominate the middle star rankings.
VI. Regression Tests: Fund Ratings and Age
A. Overall Star Ratings and Fund Age.
The data presented in Tables 1 and 2 suggest that older funds are less likely to earn extreme ratings but serious analysis requires stronger statistical tests that adjust for important sources of variation that might obscure the true relationship between a fund’s age and its Morningstar rating. Two of these sources are 1) the differential weighting of the Rk, 36 vector for funds of different ages in the calculation of the overall star rating and 2) market conditions and cycles over the return estimation period. Because of the confounded and complex nature of these two factors, adjustments can only be made indirectly. Other important sources of variation are more easily controlled. As we have discussed, large size may render a fund less likely to earn either very high or very low ratings; therefore, we include the fund’s total assets as a control variable. Jensen’s alpha and managerial tenure are variables that measure managerial skill and consistency, factors that can also clearly affect a fund’s star ratings, in addition to its age. Therefore, we incorporate these measures into our model as additional control variables. We use the following basic regression model to estimate the relationship between a fund star rating and its age:
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ratingi = β0 + β1YOUNGi + β2SEASONEDi + β3SIZEi +β4JENSENi + β5TENUREi + εi
In our first test, rating is the fund’s overall star rating. YOUNG is a dummy variable that takes a value of one if a fund has only three years of data (in which case, its overall star rating and its three-year time-specific rating are identical.) Otherwise, YOUNG is zero. Similarly, SEASONED is a dummy variable that takes a value of one if a
fund has a ten-year time-specific rating and is equal to zero if it does not. SIZE is the natural log of the fund’s total net assets, in dollar terms, as of June 30, 2002. JENSEN is the natural log of Jensen’s alpha, which Morningstar computes over the previous threeyear period using the S&P 500 as the market index. TENURE is the natural log of the number of years that have passed without a significant change in the fund’s top management, as reported on the Principia disk. This model is equivalent to running three simultaneous, parallel regressions. The model allows the intercepts to differ for each age group, but constrains the coefficients on the control variables, (fund size, Jensen’s alpha, and managerial tenure), to be identical across the cohorts. The β0 coefficient gives the mean overall star rating of the middleaged funds10. the If the β1 coefficient is positive, it indicates that the average overall star rating of younger funds is higher than that of middle-aged funds, other things being equal. Similarly, if β2 is positive, it indicates that older funds have higher ratings than
10
βo should not be confused with the raw mean; β0 is adjusted for the log transform of control variables, which makes its actual estimate of less interest than the differences it reveals. While our results are quite robust to the transformation, using it does improve estimates of the relative differences in ratings.
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middle-aged funds. The difference in overall ratings between the young and seasoned funds is estimated by the quantity (β1-β2). Results from this first regression are presented in Table 3 and show that there is a significant, inverse effect of age on a fund’s overall star rating. The estimate for β1 indicates that the overall star ratings of young funds are, on average, about 0.28 stars higher than those of middle-aged funds and 0.44 stars higher than seasoned funds, other things equal. Similarly, middle-aged funds have overall star ratings that tend to be about 0.16 stars higher than seasoned funds. Furthermore, large t statistics for fund size, Jensen’s alpha, and managerial tenure show the importance of controlling for these additional sources of variation.
B. Time-Specific Ratings and Fund Age.
The regression results for test of the overall star ratings suggest that younger funds tend to gather more stars and that it is more difficult for older funds to achieve higher ratings. However, the tests in Table 3 do not fully account for the effect of the state of the market during the estimation period. A fund’s overall rating may depend on whether the estimation period includes mostly bull market returns or mostly bear market returns. Because the last 36 monthly returns (those in the Rk,36 vector) are the most influential, the state of the market during this period is likely to have an even greater influence on a fund’s overall rating. This issue is further complicated by the fact that the vector receives different weights for funds of different ages in the calculation of the overall rating.
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Funds should be evaluated over the same period of time in order to control for overall market conditions and cycles over the estimation period. By comparing the timespecific ratings of the different age groups, we hold both market conditions and the weights of the various return vectors constant. Thus, we estimate two models, one for the three-year time-specific ratings (because all sample funds have a three-year rating) and one for the five-year time-specific ratings (because only middle-aged and seasoned funds have five-year ratings). These models have the form:
3-yr ratingi = β0 + β1YOUNGi + β2SEASONEDi + β3SIZEi +β4JENSENi + β5TENUREI + εi
and: 5-yr ratingi = β0 + β1SEASONEDi + β2SIZEi +β3JENSENi + β4TENUREi + εi
All variables are defined as before. Results for the three-year time-specific rating are reported in Table 4 and are in agreement with those for the overall rating. The estimate of β1 indicates that young funds receive higher three-year time-specific ratings than middle-aged funds. A negative sign on the β2 coefficient implies that the seasoned funds have lower three-year ratings. These results are most likely due to the influence of the most recent thirty-six monthly returns on both ratings. In Table 5, the results for the test of the five-year time-specific rating are given. Here, the negative sign of β1 tells us that this time-specific rating of seasoned funds is about 0.28 stars lower than that of middle-aged funds. In sum, the results
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reported in Tables 3, 4, and 5 provide clear evidence of an inverse relationship between fund age and the star ratings, both for the overall ratings and time-specific ratings. While the inverse property of the age bias is no doubt rooted in the returnweighting algorithm, additional factors outside Morningstar’s control may be at work. Foremost among these is the fund management’s incentive to ensure that a new fund starts off with a strong performance. This gives managers a powerful incentive to manipulate the portfolio of a young fund by disproportionately filling it with those stocks for which management has the highest expectations and at the same time, temporarily waiving its expenses. The smaller size of a young fund is helpful on both fronts. Because a young fund is typically also a smaller fund, a handful of successful stocks has a proportionately greater impact on its performance, at least in the short-term11. Further, it is less painful for the fund management company to cut the expenses of a small fund than a larger one, especially if the small fund is part of a large family. These actions combine to help young funds earn higher Morningstar ratings and attract new investment dollars, making them more profitable to run12. Finally, it must also be remembered that the new Morningstar ratings compare funds to a much more representative benchmark group. A high rating does not necessarily denote high returns. In a bear market, high ratings may go to the funds that lost the least, relative to their peers.
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Wermers (2003) finds that households invest heavily in last year’s top funds, which disproportionately allocate this new bounty to momentum stocks. This is clearly a strategy tailor made to boost a small, young fund. 12 Compensation to fund management increases with fund size, but profits to investors in the fund may not. Morey (2003) and Blake and Morey (2000) show that fund managers frequently have difficulty perpetuating an initial success.
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C. Constrained Tests of the Overall Ratings and Fund Age
We examine the overall rating/three-year time-specific rating/fund age relationship using all five possible values of the three-year rating, comparing middle-aged funds to seasoned funds. These tests allow us to establish the convergence property of the age bias by comparing the overall ratings of middle-aged and seasoned funds, while holding their three-year time-specific ratings constant13. With all variables defined as before, the model is given by:
Overall ratingi = β0 + β1SEASONEDi + β2SIZEi +β3JENSENi + β4TENUREi + εi
We divided the seasoned and middle-aged funds in our sample into five groups, based on their three-year time-specific ratings. Then, we ran the model five times, first only with the funds that received five stars for their three-year time-specific rating, then just with those funds having four stars for their three-year time-specific rating, and so forth. Results are shown in Table 6 and focus on β1, the coefficient that measures the differential effect of age on funds’ overall star ratings, assuming that the funds earned identical three-year time-specific ratings14. This series of tests provides clear evidence that the fund rating methodology introduces a convergence in the star ratings. The results for funds receiving the higher three-year time-specific ratings are very revealing. In each of three cases, the middle13
Comparisons to the young funds are not feasible because the three-year time-specific ratings are identical to the overall ratings, restricting the differences we wish to detect.
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aged funds have higher overall star ratings than do seasoned funds. For funds with a fivestar three-year time-specific rating, the overall ratings of the seasoned funds is almost 0.5 stars lower than the overall rating of middle-aged funds. For funds with a four-star threeyear rating, the overall rating of the seasoned funds is about 0.14 stars lower and for three-star funds, the overall rating of seasoned funds is approximately 0.10 stars lower than middle-aged funds. All of these differences are statistically significant. For funds receiving one or two stars for their three-year time-specific rating, the pattern is reversed. The seasoned funds have higher overall star ratings. Although these differences are not statistically significant, they do suggest a trend. What is the creating this convergence? Recall that the overall ratings for older funds contain multiple time-specific ratings. Since no rating can exceed five stars, each additional time-specific rating must be five or less. Therefore, folding additional timespecific ratings into the overall rating tends to lower the overall mean for seasoned funds that have received the highest three-year ratings. The mean goes down because it can only go down. The distribution is bounded so that subsequent time specific ratings, beyond the three-year rating, cannot be more than five stars. The same effect, but in the opposite direction, is evident for funds that received only one star for the three-year time specific rating. If a fund’s three-year time-specific rating is one star, its other timespecific ratings must be must be one star or more. This tends to drive up the mean overall rating of the seasoned funds that have received a three-year rating of one star.
14
In the interest of brevity, regression coefficients for the control variables are not shown, but are similar to
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VII. Conclusions.
The revised Morningstar fund rating methodology is designed to better measure the value added by fund managers. The more narrowly defined peer groups make the rating system less vulnerable to the effect of investing fads and styles. Nonetheless, regression tests that control for the effects of fund size, as well as managerial expertise and tenure, provide evidence of a continuing age bias in the Morningstar ratings. The age bias is characterized by two key properties: a cross-sectional inverse association between fund ratings and fund age and convergence within the set of ratings received by each individual fund. With regard to the first property, it is clear that as funds age, they become less likely to earn the top ratings. Our tests show that younger funds generally receive both higher overall star ratings and higher time-specific ratings than older funds. This result is noteworthy on at least two counts. First, it firmly establishes the inverse relationship between fund age and all fund ratings, not just the overall star ratings. Secondly, the inverse association is robust to market conditions and cycles. Our tests, (based largely upon fund performance under adverse market conditions), taken together with previous research, (based largely on fund performance during favorable market conditions) show that older funds tend to receive lower ratings, regardless of bull or bear market cycles. If equity risk premiums vary with market cycles, however, this relationship may not be strictly linear. The age bias also exhibits a tendency to convergence within each individual fund’s set of star ratings, as demonstrated by our tests that control for the powerful
those reported in Tables 3, 4, and 5.
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influence of a fund’s most recent performance on its overall star rating. These tests show that older funds have higher overall ratings when the three-year rating is low and lower overall ratings when the three-year rating is high15. The convergence property is reinforced by the effect of fund size. Since larger funds are less likely to produce extreme performance, and fund size tends to increase with age, older funds are less likely to earn the top ratings. Understanding the nature of the age bias and its sources is important because it means that the star rating for funds of different ages are not perfectly comparable and neither are star ratings drawn from different time periods16. Morningstar’s practice of assigning a weight to a fund’s most recent performance according to its age, with different age groups receiving different weights, is at the root of the age bias. Adjusting the ratings for fund size could possibly reduce the bias. Under the current methodology, the benchmark peer groups for equity funds are based on the size of the firms in which the fund invests and not upon the size of the fund, itself. Small size makes the fund’s results more susceptible to manipulation, an issue that is especially important with young funds. Morningstar could reduce this problem by disregarding a young fund’s first year of performance or at least reducing it weight. On the other hand, increasing size tends to make funds more closely resemble the overall market, inhibiting their chances to produce extreme results, either unusually good or bad. Although larger size would seem to promote economies of scale, resulting in cost savings that should contribute to higher star ratings, this does not appear to be the dominant effect. Since it 15
However, only the tendency for the subsequent time-specific ratings of seasoned funds to be lower when the three-year rating is higher was found to be statistically significant in our sample.
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would be difficult, and perhaps not even desirable, to adjust the rating methodology in a way that reduces the effect of market conditions and cycles, it is more practical to change not the rating system, but how investors use and interpret the ratings. If equity risk premiums vary over time, for example, as we move between bull and bear market cycles, then ratings earned over different time periods will not be directly comparable.
16
Furthermore, an understanding of the two properties of the age bias permits the seemingly conflicting results of Blume and Morey to be easily reconciled. Blume’s conclusions are based on the inverse property and Morey’s are derived from the convergence property.
24 Table 1. Distribution of Overall Star Ratings by Age of Fund
Panel A. Cells show number of funds in each group. Overall Stars
Young
Middle Aged
Seasoned
Total
*****
118
142
49
309
****
278
364
196
838
***
455
623
262
1340
**
318
429
177
924
*
108
178
57
343
Total
1277
1736
741
3745
Panel B. Cells show percentage of funds in each group. Overall Stars
Young
Middle Aged
Seasoned
*****
9.24%
8.18%
6.61%
****
21.77%
20.97%
26.45%
***
35.63%
35.89%
35.36%
**
24.90%
24.71%
23.89%
*
8.46%
10.25%
7.69%
Total
100%
100%
100%
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Table 2. Total Dollar Value of Funds by Age and Overall Star Rating
Overall Stars
Young
Middle Aged
Seasoned
Total
*****
25554
86618
223425
335597
****
35719
142454
616850
795023
***
64614
170427
403553
638594
**
23939
77913
135166
237017
*
4469
26948
23334
54751
Total
154295
504360
1402328
2060983
Cell values represent $10 MM
26
Table 3. The Relationship between Overall Star Ratings and Fund Age
Model: ratingi = β0 + β1YOUNGi + β2SEASONEDi + β3SIZEi +β4JENSENi + β5TENUREi + εi
Variables
Regression Coefficients
Intercept
2.1356 (29.04)***
YOUNG
0.2767
=1 if fund is only three years old
(6.01)***
=0 otherwise SEASONED
-0.1623
=1 if fund is at least ten years old
(-2.84)***
=0 otherwise SIZE
0.1202 (11.21)***
JENSEN
0.0967 (5.98)***
TENURE
0.2069 (6.73)***
N
2853
R-Squared
0.08
*** indicates significance at the 1% level, t statistics in parentheses.
27
Table 4. The Relationship between Three-Year Time-Specific Ratings and Fund Age Model: 3-yr ratingi = β0 + β1YOUNGi + β2SEASONEDi + β3SIZEi +β4JENSENi + β5TENUREi + εi
Variables
Regression Coefficients
Intercept
2.3372 (31.06)***
YOUNG
0.2403
=1 if fund is only three years old
(5.19)***
=0 otherwise SEASONED
-0.1292
=1 if fund is at least ten years old
(-2.52)***
=0 otherwise SIZE
0.0705 (6.54)***
JENSEN
0.1205 (7.42)***
TENURE
0.1738 (5.62)***
N
2853
R-Squared
0.05
*** indicates significance at the 1% level, t statistics in parentheses.
28
Table 5. The Relationship between Five-Year Time-Specific Ratings and Fund Age
Model: 5-yr ratingi = β0 + β1SEASONEDi + β2SIZEi +β3JENSENi + β4TENUREi + εi
Variables
Regression Coefficients
Intercept
1.965 (22.42)***
SEASONED
-0.2790
=1 if fund is at least ten years old
(-4.70)***
=0 otherwise SIZE
0.1579 (11.65)***
JENSEN
0.0920 (4.34)***
TENURE
0.2109 (6.26)***
N
1690
R-Squared
0.11
*** indicates significance at the 1% level, t statistics in parentheses.
29
Table 6. Mean Overall Ratings by Age, Controlled for Level of the 3-Year Rating
Overall ratingi = β0 + β1SEASONEDi + β2SIZEi +β3JENSENi + β4TENUREi + εi
SEASONED = 1 if the fund is at least 10 years old, otherwise the fund is middle-aged. The model is run five times, first only with the funds that received five stars for their three-year time-specific rating, then just with those funds having four stars for their threeyear time-specific rating, and so forth.
Three-Year Rating
β1 Estimates
5 stars
-.4633
***
4 stars
-.1383
**
3 stars
-.0980
*
2 stars
0.0240
No significant difference
1 star
0.0683
No significant difference
Significance level for t-test of pair
* indicates significance at the 10% level **indicates significance at the 5% level *** indicates significance at the 1% level
30
References
Adkisson, J. A. and D. R. Fraser. 2003. Reading the Stars: Age Bias in Morningstar Ratings. Financial Analysts Journal, September/October, 24-27. Adkisson, J. A., and D. R. Fraser. 2003. “Realigning the Stars: The Reaction of Investors and Fund Managers to Changes in the Morningstar Rating Methodology for Mutual Funds.” Texas A&M University, working paper. Arnott, Robert. and P. Bernstein. 2002. “What Risk Premium is ‘Normal’?” Financial Analysts Journal, vol.58, no.2 March/April, 64-85. Bagnoli, M. and S. Watts. 2000. “The Effects of Relative Performance on Portfolio Choice.” Financial Management, vol. 28, no.3, 31-51. Blake, Christopher and M. Morey. 2000. “Morningstar Ratings and Mutual Fund Performance.” Journal of Financial and Quantitative Analysis, vol. 35, no 3, September, 451-483. Blume, Marshall E. 1998. “An Anatomy of Morningstar Ratings.” Financial Analysts Journal, vol. 54, no. 2, March/April, 19-27. Del Guercio, Diane and P. Tkac. 2001 “The Effect of Morningstar Ratings on Mutual Fund Flows,” Federal Reserve Bank of Atlanta Working Paper. Jain, Prem and J. S. Wu. 2000. “Truth in Mutual Fund Advertising: Evidence on Future Performance and Fund Flows.” Journal of Finance, vol.60, no. 2, 937-958. Morey, Matthew. 2002. “Mutual Fund Age and Morningstar Ratings.” Financial Analysts Journal, vol. 58, no.2, March/April, 56-63. Morey, Matthew. 2003. “The Kiss of Death: A 5-Star Morningstar Mutual Fund Rating?” Pace University, working paper. Sharpe, William. 1997. “Morningstar’s Performance Measures.” http://wwwsharpe.stanford.edu/stars0.htm Wermers, Russ, 2000, Mutual Fund Performance: An Empirical Decomposition into Stock-Picking Talent, Style, Transaction Costs and Expenses, Journal of Finance 55, 1655-1703. Wermers, Russ, 2002, Is Money Really ‘Smart’? New Evidence in the Relation Between Mutual Fund Flows, Manager Behavior, and Performance Persistence, working paper, University of Maryland.
