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In a recent Advisor Perspectivesarticle, Joe Tomlinson reported evidence showing that 401(k) plan sponsors add value in selecting funds, but their risk-adjusted alpha is not enough to beat a comparable index portfolio. Tomlinson then pointed out the need for additional research to help advisors improve upon the fund selection process.
As a step in this direction, I will report on research conducted by my firm and other academics.
A tour through this research provides insight into how to select actively managed equity funds. This research shows a 500-basis point difference in after-the-fact excess returns between the best and worst funds. There are a number of general lessons that advisors can apply to increase their chances of selecting a superior active equity fund.
Fund diamond rating
Our research categorizes the marketplace of active equity mutual funds into peer groups on the basis of investment strategy. We determine each fund’s strategy based on how the fund manager selects and sells stocks in pursuit of excess return.
Within each peer group, we rank funds into five tiers known as fund diamond ratings (DRs). Mutual funds that consistently pursue their strategy and take high-conviction positions within the portfolio garner a diamond rating 5 (DR5), while funds scoring the least among these dimensions earn a diamond dating 1 (DR1).
DRs rely exclusively on the investment decisions of active equity managers, who are placing actual bets with monetary consequences for future performance. We do not employ in-house analysts to refine or second-guess professional mutual fund managers and analysts. Instead, we objectively measure the extent to which a fund manager sticks to a strategy and remains highly convicted.
Mutual fund companies devote significant resources to research equity markets, more than individual investors or most investment professionals can afford. If funds stay true to their stated strategy and invest heavily in their best idea stocks, they have an excellent chance of outperforming their peers. Our research consistently supports this premise.
DRs measure the extent to which managers are truly active in terms of stock selection. A common challenge facing mutual fund investors is avoiding closet index funds. This is a real difficulty — many closet indexers are large, well-known funds that appeal to investors on the basis of brand identity. By paying attention to DRs, though, investors can build portfolios comprised of truly active funds and avoid the costly mistake of investing in return-denigrating closet index funds. Such a mistake could cost the investor hundreds of basis points in return over the long run.
Berk and Green (2004) argued that when fund complexes act rationally, they transform their products into closet funds, as this maximizes firm revenues and profits. DRs measure this life cycle of a typical active equity fund and help investor avoid being swamped by this powerful industry tsunami.
Strategy consistency
After a fund’s strategy, the next most important consideration is how consistently the strategy is pursued over time. We created an objective measure of strategy consistency based on the proportion of stocks held by a manager that correlate to those owned by their strategy peers.
For example, stocks most attractive to valuation managers (one of the peer groups we defined) are referred to as valuation stocks. Similarly, the stocks most attractive to managers pursing the future growth strategy are referred to as future growth stocks. Thus each stock becomes a member of a particular strategy “stock pool,” based on which strategy finds the stock most attractive.
Strategy pool characteristics, such as average market cap and price-to-earnings ratio, change over time as managers within each strategy peer group adjust their holdings. Strategy stock pools are defined by the manager peer groups (strategies) that hold them, not the other way around.
Our historical data show that a stock will remain in any given pool for an average of 13 months, with market conditions stocks changing most frequently and valuation stocks being the most stable.
A fund’s strategy consistency measure is based on the percent of stocks from the stock pool that the fund holds. This does not mean that a fund must hold only own strategy stocks in order to be highly rated, but a higher percentage is preferable.
Why does this matter? The fund develops a strategy for earning superior returns and builds an investment team around the strategy. In turn, a fund gets the best results by focusing its considerable resources on those stocks best understood in terms of the strategy. (This does not necessarily mean that every fund in a strategy peer group holds the same stocks, since there are often hundreds of stocks in a given strategy pool. Two funds can hold completely different stocks and yet both demonstrate strategy consistency.)
The predictive power of the strategy consistency measure (with 1 representing lowest consistency and 10 representing highest consistency) is confirmed in Figure 1. After-the-fact fund returns generally increase with higher consistency. The annualized return advantage (slope of returns regressed on consistency) is 4.99%, which means that strategy-consistent funds produce significantly higher returns than those that lack consistency.
Based on strategy-identified U.S. and international active equity mutual funds domiciled in the U.S. January 1999 – July 2011, resulting in 403,211 monthly fund-return observations. Consistency measures 1 and 10 are not reported due to substantially smaller sample sizes. Fund returns are net of automatically deducted fees.
Data sources: AthenaInvest and Thomson Reuters.
Strategy conviction
Beyond strategy consistency, a fund must also take high-conviction positions. A number of studies demonstrate that high-conviction portfolios, also known as concentrated equity, outperform more diversified fund counterparts. These studies conclude better portfolio performance comes from a number of factors: 1
- Lower fund r-squared
- More concentrated portfolios
- Smaller funds –Higher active share
- Higher industry concentration
- Greater style drift
The superior fund performance reported in these studies can be attributed to fund managers’ stock selection skill. Indeed, there is a growing body of research revealing that stock-picking skill is much more common than previously thought. This research concludes:2
- The higher the relative portfolio rank, the higher is the stock return
- Institutional managers have stock selection skill
- Buy-side analysts are superior stock pickers
- Funds that respond less to public information earn superior returns
- Unobserved trades produce superior returns
- Mutual fund “stars” are superior stock pickers)
- Best idea trades produce superior returns
- Fund holdings contain valuable stock return information
- Fund managers display stock picking skill
- Fund holdings can be used to build superior stock portfolios
Our strategy-conviction measure is based on the findings of these studies. We place the least convicted funds in decile 1 and the most convicted funds in decile 10.
Figure 2 reports the subsequent returns for funds based on their beginning-of-month strategy-conviction measure. The conviction results are as strong as the previously reported consistency results, though results rise more monotonically across strategy conviction measures than across consistency measures. The annualized return advantage is 6.04% between the top and lowest deciles, which is the same order of magnitude as is the consistency return advantage. This indicates that consistency and conviction are roughly equivalent in their predictive power. Thus, we use both when assigning a fund’s DR.
Based on strategy-identified U.S. and international active equity mutual funds domiciled in the U.S. January 1999 – July 2011, resulting in 397,735 monthly fund-return observations. Fund returns are net of automatically deducted fees.
Data sources: AthenaInvest and Thomson Reuters.
1. Specific references for each factor, listed in the order they appear above: Amihud and Goyenko (2012), Brands et al. (2006), Chen et al. (2004), Cremers and Petajisto (2009), Kacperczyk et al. (2005), and Wermers (2010).
2. Specific references for each result, listed in the order they appear above: Cohen et al. (2010), Collins and Fabozzi (2000), Frey and Herbst (2010), Kacperczyk and Seru (2007), Kacperczyk et al. (2008), Kosowski et al. (2006), Pomorski (2009), Shumway et al. (2009), Wermers (2000), and Wermers et al. (2010).
The correlation between Diamond Rating and performance
Active equity mutual funds are roughly distributed across DRs, as can be seen in Figure 3, which reports recent fund totals.
Based on 2,465 U.S. and International Active Equity Mutual funds domiciled in the U.S., ignoring share classes.
Data source: AthenaInvest data base.
The higher the DR, the more likely it will outperform in the future. The superior performance of higher rated funds is evident in Table 1. DR5 funds outperform DR1 funds by more than 5% annually, based on one-year subsequent returns, and they continue to deliver outperformance up to five years after the initial rating was assigned. In this fashion, DR1 and DR2 funds underperform the market, DR3 funds perform at the market, and DR4 and DR5 funds outperform. The average fund matches market performance over the entire time period, consistent with results reported by Bollen and Busse (2004), Brown and Goetzmann (1995) and Fama and French (2010), among others.
Thus, strategy consistency and conviction are predictive of future fund performance for up to five years after the rating is assigned.
Based on subsequent monthly returns for beginning-of-the-month U.S. and international-strategy-identified, Diamond Rated active equity mutual funds April 1997 through July 2009. DR is based on Strategy Consistency and Conviction, with DR5 being the highest on both scales and DR1 being the lowest. Fund returns are net of automatically deducted fees and the S&P 500 return. Other than the 1-month return, all returns are annual, compound. The DR 5-1 return advantage is calculated as the annualized slope from the regression of DR returns on a variable taking on the values 1 through 5.
Data sources: AthenaInvest and Thomson Reuters Financial.
Fund Diamond Rating persistence
DR persistence is presented in Table 2 as the number of months a fund will sustain its rating after being ranked. The first column examines how long DR5 funds keep that rating, while the second column includes funds that maintain DR5 ratings or drop to DR4 and the third column looks at DR5 funds that are later rated as DR5, 4, or 3. On average, a fund remains a DR5 for eight months, a DR5/4 for 40 months and a DR5/4/3 for 85 months. Fund DRs are strongly persistent, an important driver of the return persistence shown in Table 1.
The values reported in Table 2 are downward biased because more recent data was not available. The sample ends on September 2012, thus artificially cutting short the number of months in a DR. There is no easy way to adjust for this downward bias, but it could be significant, particularly for the longer DR5/4/3 persistent estimates.
Based on all U.S. and international-strategy identified, Diamond Rated active equity mutual funds April 1997 through September 2012. Values are the number of months that a fund remains a DR5, DR5 or 4, and DR5 or 4 or 3 since first rated DR5.
Data source: AthenaInvest and Thomson Reuters Financial.
Concluding observations
The three keys to selecting superior performing active equity mutual funds are strategy, consistency and conviction. Strategy — how a manager analyzes, buys and sells stocks to earn superior returns — is the foundation upon which a fund builds and provides resources for its investment team. Strategy, the fund’s core competency, is best focused on stocks that reflect the strategy. It is also very important for the fund to take high conviction positions based on their best-idea stocks. These best-idea stocks, if not drowned out by other stocks, generate superior returns and, in turn, lead to superior fund returns.
A major challenge facing advisors is avoiding closet indexers, which make up at least 70% of the active equity mutual fund universe (see Cremers and Petajisto, 2009). Such funds display high r-squared, low tracking error, low style drift, low active share, broad diversification and brand name recognition. In other words, these funds appeal to our emotional need for social validation of our investment selections. It is a considerable challenge to steer clear of such funds, but the potential rewards for our clients are worth the extra effort.
Tom Howard is CEO and director of research for Denver-based AthenaInvest and professor emeritus at the University of Denver.
References
Amihud, Yakov and Ruslan Goyenko, 2012.Mutual Fund’s R2 as Predictor of Performance. Forthcoming Review of Financial Studies.
Baker, Malcolm, Lubomir Litov, Jessica A. Wachter, and Jeffrey Wurgler. 2004. Can Mutual Fund Managers Pick Stocks? Evidence from Their Trades prior to Earnings Announcements. NBER Working Paper w10685 (July 28).
Berk, J. B. and R. C. Green, 2004. Mutual Fund Flows and Performance in Rational Markets. Journal of Political Economy, vol. 112, no. 6, 1269-1295.
Bollen, N. P. B. and J. A. Busse, 2004. Short-Term Persistence in Mutual Fund Performance. Review of Financial Studies, vol. 18, no. 2, 569-597.
Brands, S., Brown, S.J. and D.R. Gallagher, 2006. Portfolio Concentration and Investment
Manager Performance, International Review of Finance, 149-174.
Brown, S. J. and W. N. Goetzmann, 1995. Performance Persistence. Journal of Finance, vol. 50, no. 2, 679-698.
Chen, J. , H. Hong, M. Huang, and J. D. Kubik, 2004. Does Fund Size Erode Performance? Organizational Diseconomies and Active Money Management. American Economic Review, vol. 94, no. 5, 1276-1302.
Cohen, R. B., C. Polk, and B. Silli, 2010. Best ideas. Harvard Working Paper. March.
Collins, Bruce and Frank Fabozzi, 2000. Equity Manager Selection and Performance. Review of Quantitative Finance and Accounting. 15, 81-97.
Cremers, Martijn and Antti Petajisto, 2009. How Active Is Your Fund Manager? A New Measure That Predicts Performance. Review of Financial Studies, September, pp 3329-3365.
Fama, Eugene F. and Kenneth R. French, 2010. Mutual Fund Performance. University of Chicago Working Paper, August.
Frey, Stefan and Patrick Herbst, 2010. The Influence of Buy-side Analysts on Mutual Fund Trading. University of Tṻbingen Working Paper, January.
Kacperczyk, M. T. , C. Sialm, and L. Zheng, 2005. On Industry Concentration of Actively Managed Equity Mutual Funds. Journal of Finance, vol. 60, no. 4, 1983-2011.
Kacperczyk, M. T. , and Amit Seru, 2007. Fund Manager Use of Public Information:
New Evidence on Managerial Skills. Journal of Finance, April, 485-528.
Kacperczyk, M. T. , C. Sialm, and L. Zheng, 2008. Unobserved Actions of Mutual Funds. Review of Financial Studies, (November), 21, 2379 – 2416.
Kosowski, Robert, Allan Timermann, Russ Wermers, and Hal White, 2006. Can MutualFund “Stars” Really Pick Stocks? New Evidence from a Bootstrap Analysis. Journal of Finance, December , 61-6, 2551-2595.
Pomorski, Lukasz, 2009. Acting on the Most Valuable Information: Best Idea Trades of Mutual Fund Managers, (March), University of Toronto working paper.
Shumway, Tyler, Maciej Szeter, and Kathy Yuan, 2009. The Information Content of
Revealed Beliefs in Portfolio Holdings, (January ) University of Michigan working paper
Wermers, R. , 2000. Mutual Fund Performance: An Empirical Decomposition into Stock-Picking Talent, Style, Transactions Costs, and Expenses. Journal of Finance, vol. 55, no. 4, 1655-1695.
Wermers, R., 2010. A Matter of Style: The Causes and Consequences of Style Drift in Institutional Portfolios. (May) working paper, University of Maryland.
Wermers, R., Tong Yao, and Jane Zhao, 2010. The Investment Value of Mutual Fund Portfolio Disclosure (December), working paper, University of Maryland.
Read more articles by C. Thomas Howard, PhD
