Many news and industry reports proclaimed 2015 to be a challenging year for active managers. For U.S. large-cap stock funds in particular, actively managed funds on average have tended to underperform their benchmarks, and 2015 was no exception. But what if those overall averages were not really representative of the active funds investors tend to favor?
In 2015, Fidelity published a research article addressing that very question about actively managed mutual funds.1 That research found that historically, using two objective criteria as filters could have narrowed the wide range of U.S. large-cap stock funds down to a smaller group that, on average, outperformed the industry as a whole and outperformed their benchmarks. In that article, analysis of historical returns for all U.S. large-cap funds from 1992 to 2014 showed that the average actively managed fund underperformed its benchmark by more than the average passive index fund did.
However, by using two objective filters to select only funds with the lowest fees from the five largest mutual fund families by assets under management (representing funds with greater potential resources), the average active fund out performed. Although averages are no guarantee of the performance of any individual fund, these results suggest that investors could benefit from using these simple filters to help select a set of appropriate funds for further consideration. (Note: These filters were chosen by Fidelity; had other filters or filter parameters been used, results would have been different.)
The analysis focused on U.S. large-cap equity funds partly due to high investor interest in that category, and partly due to a commonly held belief that a high degree of market efficiency in this category makes it more challenging for active stock pickers to outperform a broad market benchmark. (For analysis of two other market categories, see Exhibit A chart at end in the section: Outside of U.S. large-cap, average active funds outperform.)
2015 results support the conclusion
New data for 2015 further supported the conclusions of this research (see Exhibit 1 chart below). Looking just at the one-year results, the average actively managed fund did worse than the long-term average, earning 137 basis points less than its benchmark, after fees.2 In contrast, the average active filtered fund did better than the long-term average: the average low- fee fund was only 38 basis points below the benchmark, while the average fund from the top five fund families earned 54 basis points above the benchmark. With both filters combined, the average active mutual fund earned 70 basis points above the benchmark for 2015 alone, compared with a long-term average of 18. In other words, 2015 was a challenging year for actively managed U.S. large-cap equity funds in general, but the average low-fee fund from the largest fund families did much better, outperforming its benchmark by 70 basis points.
Over the longer term, with both filters in place for both types of funds, the average active fund outperformed its bench- mark (see Exhibit 1 chart below) while the average passive index fund still lagged its benchmark slightly (as one would expect from passive funds, which seek to match benchmark performance before fees). These results suggest that industry-average figures for both active and passive funds may not fairly represent the investment performance achieved by many investors.
Understanding the filters
The fee and fund-family size filters were chosen to be objective, straightforward, and intuitive. The fee filter selects funds in the lowest 25% of reported expense ratios for each fund type (active or passive).3 Funds with lower total expense ratios are able to deliver more of their gross returns to investors after fees. Because fees are clearly disclosed, investors can use this information to help them select funds. For 2015, the average filter cutoff for the lowest-fee funds was 79 basis points for actively managed funds, and 11 basis points for passive funds.The size filter focuses on assets under management (AUM), considering AUM to be a reasonable proxy for scale. For active funds, the filter selected funds from the mutual fund families with the most assets in active U.S. large-cap equity funds, because larger fund companies could use size to their advantage by committing more resources to research and trading, and the benefits of those resources can be shared across all the companies’ active U.S. large-cap funds. For passive index funds, the filter selected the top 10% of funds by size, in order to confer a similar selectivity and potential advantage (see endnotes for more information).
In the mutual fund industry, differences in fund family size can be quite large. At the end of 2015, the median amount of actively managed U.S. large-cap assets for all fund families was around $243 million, while the median for the top five fund families was more than $180 billion—more than 740 times larger.4 The largest five fund families held approximately 49% of the industry’s assets, while the smallest 50% of fund families (173 of 345) held less than 0.5% of AUM.As a result, any average analysis of the entire industry will include a high proportion of active fund families that may lack comparable resources to compete.
Better average performance is consistent with filters
Although past performance is no guarantee of future results, these filters have been remarkably consistent in identifying sets of funds with above-average relative performance over time. For rolling three-year returns, the average actively managed fund selected by both filters beat the industry average a full 98% of the time. Exhibit 2 shows how consistent this outperformance was, and by how much. In addition, a statistical test indicates one can be 99% certain that the historical long-term outperformance of the filtered average fund relative to the industry is significant.5
Implications for investors
Although these filters are not the only way to search for better-performing actively managed U.S. large-cap stock funds, many investors may find it useful to know that these simple objective criteria succeeded in identifying a subset of actively managed funds that has performed better than the general averages and outperformed their benchmarks on average (while a comparably selected subset of passive index funds still underperformed).
Just as important, even though 2015’s average actively managed fund did worse than the long-term average, the average filtered fund did much better than the average fund for the year, and beat its benchmark by 70 basis points after fees. Of course, averages never tell the whole story, and any one particular fund may do better or worse than the average, particularly over short time horizons. Prudent, informed research is always an important part of identifying funds that fit an investor’s objectives. However, we believe the results of applying these criteria continue to suggest that certain straightforward and objective filters can be a helpful starting point for investors seeking to identify above-average actively managed equity funds.
Outside of U.S. large cap, average active funds outperform
U.S. large-cap stock funds are a commonly cited example of how difficult it is for active funds to outperform. Fidelity’s research has shown that in the other largest equity fund categories (international large-cap equity and U.S. small-cap equity), active managers have had a better record of outperforming their benchmarks, even when all funds are considered (see Exhibit A on right).
1 See Leadership Series article ”U.S. Large-Cap Equity: Can Simple Filters Help Investors Find Better-Performing Actively Managed Funds?” (May 2015).
2 A basis point is 1/100th of percentage point. So, 137 basis points is 1.37%.
3 Expense ratio is the total annual fund operating expense ratio as reported in the fund’s most recent prospectus.
4 Data as of December 31, 2015. Source: Morningstar, Fidelity Investments.
5 After making an adjustment for overlapping data, the statistical significance of this outperformance was evaluated in a two-tailed test, and resulted in a t-statistic of 4.43. A two-tailed test is a method for computing the statistical significance of a parameter inferred from a data set, in terms of a test statistic. In this case, the test statistic of 4.43 indicates a 99% likelihood that the results are significant and not random.
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Fund selection: Our main analysis focused on all U.S. large-cap, foreign large-cap (“international large-cap”), and U.S. small-cap equity mutual funds tracked by Morningstar between Jan. 1, 1992, and Dec. 31, 2015, including all blend, value, and growth funds within each category and including actively managed and passive index funds. We included funds that did not exist for the entire period (closed or merged funds), to reduce survivorship bias. We eliminated funds identified as passive that were labeled as “enhanced index,” and eliminated funds with tracking error greater than 1% (which are unlikely to be actual passive index strategies despite their identification in the database). For international large-cap funds, we eliminated funds benchmarked to a price index, for greater comparability. See below for benchmark indexes included and definitions.
Our analysis began with the entire set of funds with available data from Morningstar at any point over the full period: 2,013 actively managed mutual funds, and 115 passive index mutual funds. We selected the oldest share class for each fund as representative; where more than one share class was the oldest available, we chose the class labeled as “retail.”
For U.S. large-cap equity, average fund counts for each subset of selected funds are as follows: Unfiltered (full set of funds available): active 831, passive 50. Fee filter only: active 220, passive 13. Size filter only: active 79, passive 5. Both filters applied: active 46, passive 3. Total fund counts for international large-cap equity funds: active 432, passive 29; average fund counts for performance calculation: active 218, passive 11. Total fund counts for U.S. small-cap equity funds: active 704, passive 43; average fund counts for performance calculation: active 295, passive 18.
Averaging excess returns: We used Morningstar data on returns from Jan. 1, 1992, through Dec. 31, 2015. We calculated each fund’s excess returns on a one-year rolling basis, relative to each fund’s primary prospectus benchmark and net of reported expense ratio, for each month. We used an equal-weighted average to calculate overall industry one-year returns for each month. (We chose equal weighting for the averages in order to represent the average performance of the range of individual funds available to investors, rather than asset weighting, which may introduce bias into an analysis.) For filtered subsets of funds, average excess returns ascribed were the one-year forward rolling returns, calculated monthly. All filtered subsets were rebalanced monthly. If a fund closed or was merged during a one-year rolling period, its returns were recorded for the months that it was in existence, and the weighting of the remaining funds in the subset was increased proportionally for the remainder of the year.
Filters: We used Morningstar data on fund expense ratios to represent fees. The fee filter is rebalanced monthly; over the full period, the average cutoff for lowest quartile of fees was 79 bps for active, 11 bps for passive. The size filter is rebalanced monthly. The size filter used a different methodology for active and passive in order to generate comparable selectivity; for passive funds, using the same filter as for active funds produced an average annual excess return of –36 basis points for the filtered subset in the initial research (using data from Jan. 1, 1992, through Dec. 31, 2014, for the previously published study), while using a filter that selected for the top 10% of passive index funds by AUM (approximating the selectivity of the top five fund family filter for actively managed funds) produced a better average annual excess return of –16 basis points.
For a more detailed description of this methodology, see Fidelity Leadership Series article “U.S. Large-Cap Equity: Can Simple Filters Help Investors Find Better-Performing Actively Managed Funds?” (May 2015).
Indexes: Funds in the study included active and passive funds tracked by Morningstar and benchmarked to the following indexes: U.S. large-cap equity (all in USD): Russell 1000; Russell 1000 Growth; Russell 1000 Value; Russell 3000; Russell 3000 Growth; Russell 3000 Value; S&P 500. Foreign (international) large-cap equity (all in USD): MSCI ACWI Ex USA; MSCI ACWI Ex USA Growth; MSCI ACWI Ex USA Value; MSCI EAFE; MSCI EAFE Growth; MSCI EAFE Value; MSCI World Ex USA; MSCI World Ex USA Growth; MSCI World Ex USA Value. U.S. small-cap equity (all in USD): Russell 2000; Russell 2000 Growth; Russell 2000 Value; S&P SmallCap 600.
Before investing in any mutual fund, consider the investment objectives, risks, charges, and expenses. Contact Fidelity for a prospectus or, if available, a summary prospectus containing this information. Read it carefully.
Views expressed are as of the date indicated, based on the information available at that time, and may change based on market and other conditions. Unless otherwise noted, the opinions provided are those of the authors and not necessarily those of Fidelity Investments or its affiliates. Fidelity does not assume any duty to update any of the information. Investment decisions should be based on an individual’s own goals, time horizon, and tolerance for risk.
Past performance is no guarantee of future results.
Stock markets are volatile and can fluctuate significantly in response to company, industry, political, regulatory, market, or economic developments. Investing in stock involves risks, including the loss of principal. Foreign markets can be more volatile than U.S. markets due to increased risks of adverse issuer, political, market, or economic developments. Investments in smaller companies may involve greater risks than those in larger, more well-known companies.
Active and passively managed funds are subject to fees and expenses that do not apply to indexes. Indexes are unmanaged. It is not possible to invest directly in an index.
Excess return: the amount by which a portfolio’s performance exceeds its benchmark, net (in the case of the analysis in this article) or gross of operating expenses, in percentage points.
MSCIACWI(AllCountry World Index) ex USA Index is a market- capitalization-weighted index designed to measure the investable equity market performance for global investors of large and mid-cap stocks in developed and emerging markets, excluding the United States.
MSCI ACW I(All Country World Index) ex USA Growth (Value) Index is a market capitalization-weighted index designed to measure the investable equity market performance of growth (value) stocks for global investors of large and mid-cap stocks in developed and emerging markets, excluding the United States.
MSCI EAFE Index is a market capitalization-weighted index that is designed to measure the investable equity market performance for global investors in developed markets, excluding the U.S. & Canada.
MSCI EAFE Growth (Value) Index is a market capitalization-weighted index that is designed to measure the investable equity market performance of growth (value) stocks for global investors in developed markets, excluding the U.S. & Canada.
MSCI World ex USA Index is a market capitalization weighted index that is designed to measure the investable equity market performance for global investors of developed markets, excluding the United States.
MSCI World ex USA Growth (Value) Index is a market capitalization weighted index that is designed to measure the investable equity market performance of growth (value) stocks for global investors of developed markets, excluding the United States.
Russell 1000 Index is a market capitalization-weighted index designed to measure the performance of the large-cap segment of the U.S. equity market.
Russell 1000 Growth Index is a market capitalization-weighted index designed to measure the performance of the large-cap growth segment of the U.S. equity market. It includes those Russell 1000 Index companies with higher price-to-book ratios and higher forecasted growth rates.
Russell 1000 Value Index is a market capitalization-weighted index designed to measure the performance of the large-cap value segment of the U.S. equity market. It includes those Russell 1000 Index companies with lower price-to-book ratios and lower expected growth rates.
Russell 2000 Index is a market capitalization-weighted index designed to measure the performance of the small-cap segment of the U.S. equity market. It includes approximately 2,000 of the smallest securities in the Russell 3000 Index.
Russell 2000 Growth Index is a market capitalization-weighted index designed to measure the performance of the small-cap growth segment of the U.S. equity market. It includes those Russell 2000 Index companies with higher price-to-book ratios and higher forecasted growth rates.
Russell 2000 Value Index is a market capitalization-weighted index designed to measure the performance of the small-cap value segment of the U.S. equity market. It includes those Russell 2000 Index companies with lower price-to-book ratios and lower forecasted growth rates.
Russell 3000 Index is a market capitalization-weighted index designed to measure the performance of the 3,000 largest companies in the U.S. equity market.
Russell 3000 Growth Index is a market capitalization-weighted index designed to measure the performance of the broad growth segment of the U.S. equity market. It includes those Russell 3000 Index companies with higher price-to-book ratios and higher forecasted growth rates.
Russell 3000 Value Index is a market capitalization-weighted index designed to measure the performance of the broad value segment of the U.S. equity market. It includes those Russell 3000 Index companies with lower price-to-book ratios and lower forecasted growth rates.
S&P 500 Index is a market capitalization-weighted index of 500 common stocks chosen for market size, liquidity, and industry group representation to represent U.S. equity performance.
S&P SmallCap 600 Index is a market capitalization-weighted index of 600 small-capitalization stocks.
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