Practice What You Preach: Strategy Consistency and Mutual Fund Performance FULL
We propose a novel predictor of equity mutual fund performance, “strategy consistency”, defined as the degree to which a fund picks stocks most chosen collectively by managers with a similar self-declared principal investment strategy. Using a proprietary strategy classification based on textual analysis of fund prospectuses, we show that high-consistency funds earn significantly higher abnormal returns than low-consistency funds. Moreover, high-consistency funds with the strongest prior-month performance earn significantly positive abnormal returns of 4% per annum. Our results help explain why most mutual funds underperform their benchmarks; they pick stocks that do not closely align with their primary strategy.
1. Introduction
According to Morningstar, active U.S. equity mutual funds held $4.6 trillion in assets under management at the end of 2019. One of the most important and long-standing questions in financial markets is whether managers of any of these active funds possess sufficient skill to earn abnormal returns relative to their benchmarks. Predicting which funds will deliver superior performance ex ante is a critical input for evaluating market efficiency and allocating investor capital but is also notoriously challenging. Perhaps the most pervasive empirical fact on the performance of actively managed equity mutual funds is that, as a group, they significantly underperform their benchmarks, especially after expenses. Moreover, Fama and French (2010) and Barras, Scaillet, and Wermers (2010) estimate that only about one or two percent of active funds have nontrivial positive abnormal returns (after costs), so identifying superior funds ex ante is analogous to finding a needle in a haystack. Worse yet, Jones and Mo (2021) and De Miguel, Gil-Bazo, Nogales, and Santos (2021) show that most of the variables that historically predicted mutual fund performance fail to do so in the last one to two decades.
In this paper, we propose a novel predictor of fund performance called strategy consistency based on a previously unexplored characteristic of fund holdings that we argue should be indicative of stock-picking skill. Strategy consistency (“consistency”) is defined to be the degree to which a fund manager picks stocks in their portfolio that are most heavily invested in collectively by the group of managers with a similar self-declared principal investment strategy. Consistency should predict returns for at least three reasons. First, it reflects consensus among presumably skilled managers following a similar strategy. For example, if multiple skilled managers following a value strategy arrive at the same conclusion to purchase a given stock, it is more likely that stock was chosen wisely than if a single manager identifies it. This follows from the simple statistical fact that if multiple noisy signals convey the same message, it is simply more likely that the message is true as opposed to driven by noise. Second, over time, managers gain expertise in strategies they invest in, increasing the likelihood of future success in these strategies. If they naively extrapolate from this accumulated expertise and deviate into other strategies, they will not perform as well in expectation. This possibility is consistent with a large literature that explores the role of overconfidence in investing and explaining asset-pricing anomalies (e.g., Daniel, Hirshleifer, and Subrahmanyam, 1998, 2001; Grinblatt and Keloharju, 2001, 2009). Finally, achieving high consistency requires tilting weights from a manager’s benchmark towards the stocks favored by a particular strategy thereby indicating high degrees of “activeness” and “conviction”, both well-known harbingers of superior performance (see., e.g., Cremers and Petajisto, 2009; Amihud and Goyenko, 2013; Doshi, Elkhami, and Simutin, 2015; Cremers and Pareek, 2016; Cremers, 2017; and Antón, Cohen, and Polk, 2021).
We obtain a proprietary measure of active U.S. equity mutual fund strategy consistency, Consistency, from the asset management company AthenaInvest (hereafter Athena). Though proprietary, this measure is based on literature-standard prospectus and holdings data from the SEC and Morningstar and is constructed in three intuitive steps as follows. First, for each fund, Athena’s algorithm examines the text of the Principal Investment Strategy in the prospectus and assigns that fund into a strategy group, such as valuation, future growth, etc. Next, Athena assigns to each U.S. stock the strategy group that weights that stock most heavily. Finally, Consistency is an increasing function of the degree to which a given fund invests in stocks assigned to that fund’s strategy group, which we refer to as own-strategy stocks. Said differently, Consistency is a function of the degree to which a fund manager invests in own-strategy stocks.
We sort funds into five portfolios based on Consistency and find that high-Consistency funds earn significantly higher raw, benchmark-adjusted, and multifactor abnormal returns than low-Consistency funds by 1.9% to 3.6% per year. Before fees captured by the expense ratio (but after trading costs), high- Consistency funds earn significantly positive abnormal returns, indicating that consistency is evidence of skill (e.g., Berk and van Binsbergen, 2015). The performance of high-Consistency funds presents even though our sample period, 2007 through 2019, was especially bad for active funds as a group. For example, over this time, we find that the typical fund underperformed its benchmark even before costs and that highly active funds underperform funds with low levels of activeness, contrary to the result in the earlier sample period of Cremers and Petajisto (2009) and Amihud and Goyenko (2013). Sorting funds into portfolios based on both Consistency and past abnormal returns shows that high-Consistency funds that have performed well in the past continue to exhibit superior performance, with (net-of-costs) alphas over 4% per year with respect to the four-factor model of Cremers, Petajisto, and Zitzewitz (2013). Overall, our results are consistent with strategy consistency helping to identify the latent manager characteristic of skill.
Our study contributes to the growing literature that attempts to predict mutual fund performance. Recent studies in this vein largely focus on measures of managerial “activeness”, i.e., the degree to which mutual fund portfolio weights deviate from those of their benchmark, and show that they predict fund performance (e.g., Kacperczyk, Sialm, and Zheng, 2005; Cremers and Petajisto, 2009; Amihud and Goyenko, 2013; Doshi, Elkhami, and Simutin, 2015; and Cremers and Pareek, 2016). Activeness is a necessary condition for superior mutual fund performance because managers cannot beat their benchmarks by copying them. As noted by Cremers (2017), however, activeness is not a sufficient condition since it does not directly measure the skill managers have to pick stocks, only the amount of stock-picking they actually do. De Miguel, Gil-Bazo, Nogales, and Santos (2021) use a machine-learning approach to combine many previously documented predictors of mutual fund performance, but find that, unlike Consistency, these predictors perform poorly during our sample period. Historically, a vast literature predicts returns on funds with past returns based on the premise that, if skill persists from one period to the next, then performance should too (see, e.g.., Bollen and Busse, 2005 for a recent survey). For example, Sirri and Tufano (2002), Del Guercio and Tkac (2008), and Berk and van Binsbergen (2016) show that past performance largely drives fund flows from investors. However, previous studies generally find that performance persistence is a short-lived phenomenon and past performance does not subsume other forward-looking information. For example, Armstrong, Genc, and Verbeek (2019) show that Morningstar ratings that incorporate analyst reports help predict funds with superior performance.
Another important finding of this paper is that, examining the correlations between the returns on all pairs of funds in the sample, we find funds’ returns correlate more heavily with other funds following the same strategy than they do with funds in other strategies. No strategy classification scheme is perfect, but our correlation evidence vindicates the proprietary strategy classification used by Athena because it shows same-strategy managers pick economically related stocks. Ben-David, Li, Rossi, and Song (2020) find that investor fund flows “chase” Morningstar ratings, which are based on past performance. If these ratings are not adjusted for groupings, or “styles”, of stocks in which there is a high degree of correlation, then ratings can cause a style momentum effect. The basic cause is that high within-style correlation inflates the perceived attractiveness of all funds in a successful style, thereby causing non-fundamental capital flows into these funds. Our correlation results suggest that investors and ratings providers should be careful to consider fund returns relative to other funds following similar strategies as well. Recognizing the importance of strategy-based fund categorization that is more comprehensive than commonly used alternatives like investment objective or the two-dimensional Morningstar Equity Style Box, Brown and Goetzman (1997) propose a statistical clustering-based measure of strategy categorization, though noting the limitation that the category boundaries lack economic motivation. We expand on this literature by providing a strategy classification based on economically motivated clustering and demonstrating the significant performance implications of maintaining consistency with respect to a given fund’s strategy.
The rest of this paper is organized as follows. Section 2 describes our data sources and variable construction. Section 3 presents our main results and Section 4 concludes.
2. Data and variable construction
2.1. Data sources
2.2. Strategy and strategy consistency
2.2.1. Equity strategy identification
Investment strategy is the way a manager goes about analyzing, buying, and selling stocks. No two fund managers have identical strategies, even if they pick stocks based on economically similar variables. For example, two managers following a value strategy both try to find “undervalued” stocks, but they will not, in general, hold the same portfolio. However, despite these differences, it is critical to categorize funds into strategy peer groups based on objective empirical criterion for the purposes of performance evaluation. The performance of most fund managers is evaluated relative to a strategy-related benchmark and managers face a well-known moral hazard problem if they can choose their own benchmark. Moreover, if performance metrics are not adjusted for strategy, then investors can allocate capital to funds, regardless of skill, if a common factor drives these funds’ returns and performs well (e.g., Ben-David et al., 2020). For example, if value stocks outperform growth stocks over a period, investors may naively invest in a value fund, even if that fund underperformed its value peers. Brown and Goetzman (1997) and Chan, Chen, and Lakonishok (2009) also show that common fund peer groups, such as the prospectus investment objective or Morningstar equity style grid are not necessarily well defined and are, in general, too course, leaving out important sources of common variation in the cross-section of mutual fund returns.
We argue that categorizing managers into economically motivated strategy groups based on their self-declared principal investment strategy is a natural choice. For example, managers claiming to follow a value strategy should have similar performance benchmarks. Athena strategy identifies active equity mutual funds by gathering the text of the “Principal Investment Strategies” section of each fund’s prospectus and inputting this text into a proprietary strategy identification algorithm. This algorithm was developed using an iterative process involving manager interviews, gathering principal strategy information, eliminating key words that generated false signals, and creating a manageable number of strategies. The Principal Investment Strategies prospectus statement was first mandated by the SEC in 1998. After extensive testing, Athena finalized its algorithm by the start of 2007 and then backfilled their data to prior years. To avoid selection bias, we do not use observations prior to 2007, although untabulated tests show this choice does not impact our main inferences. To ensure accuracy, the strategy identifications assigned by the algorithm are subjected to a series of audits before being included in the fund data base. Athena updates the strategy classification whenever a fund issues a revised prospectus.[3] The strategy identification algorithm searches the principal investment strategy’s text for key words and phrases that are matched with 40 “elements”, which are specific items or concepts that managers use to pursue their strategy. Funds are then assigned to one of 10 equity strategies based on the combination of elements they use.
Panel A of Table 1 lists and describes the 10 equity fund strategies and Panel B lists the 40 strategy elements. Panel A shows, for example, that ‘Competitive Position’ managers focus on business principles, including quality of management, market power, product reputation, competitive advantage, sustainability of the business model, and history of adapting to market changes. On the other hand, ‘Economic Conditions’ managers take a top-down approach based on economic fundamentals which might include employment, productivity, inflation, and industrial output, then gauge where the overall economy is in the business cycle, the resulting supply and demand situations in various industries, and the best stocks to purchase as a result. While the exact algorithm used to map the principal investment strategy text onto the elements and then combine elements into strategies is proprietary, Panels A and B both shows that these elements and strategies are based on ubiquitous concepts used throughout the asset pricing and investments literature. We provide further empirical validation of these classifications below as well and, unlike other proprietary data used in the literature, Athena’s measures are available at no cost to any researcher conditional on signing and non-disclosure agreement. Our strategy consistency variable can also be constructed using any alternative strategy classification.
Table 1: Active equity mutual fund strategies
AthenaInvest assigns strategies to mutual funds using an algorithm that analyzes the text of the ‘principal investment strategies’ section of the funds’ prospectuses. The algorithm searches the principal investment strategy’s text for key words and phrases that are matched with 40 “elements”, which are specific items or concepts managers use to pursue their strategy. Funds are then assigned to one of 10 equity strategies based on the combination of elements they use. Panel A lists the ten mutual fund strategies. Panel B lists the 40 strategy “elements”.
Table 1: (continued)
2.2.2. Strategy consistency
After the fund strategy identification defined above, Athena constructs its strategy consistency measure, Consistency, in two steps, described in detail in this section. First, they strategy identify stocks based on which strategy’s funds invest in them most heavily. Second, they define Consistency for a mutual fund based on how much of its portfolio consists of own-strategy stocks.
After the fund strategy identification defined above, Athena constructs its strategy consistency measure, Consistency, in two steps, described in detail in this section. First, they strategy identify stocks based on which strategy’s funds invest in them most heavily. Second, they define Consistency for a mutual fund based on how much of its portfolio consists of own-strategy stocks.
For each stock, 𝑖, month, 𝑡, and strategy, 𝑆, Athena sums the weight, W 𝑖,𝑗,𝑡 , across all funds, 𝑗, with strategy 𝑆 and AUM of $1 billion or less:
(1)
Using portfolio weights, rather than absolute amounts, eliminates the effect of fund size. Focusing on smaller funds (AUM ≤ $1 billion) helps avoid economically uninteresting variation in weights caused by the high degrees of diversification and benchmark-tracking common in large funds.9 They then scale the sum given by Eq. (1) by dividing it by the average 𝑠𝑢𝑚 𝑖,𝑆,𝑡 for stocks in that strategy to temper the mechanical effect of the number of funds in the strategy, 𝑁𝑡 𝑆 :
(2)
Athena then defines the strategy profile for stock-strategy-month (𝑖, 𝑆,𝑡) by normalizing the 𝑠𝑢𝑚̃ 𝑖,𝑆,𝑡 to sum to 100% across the ten strategies:
(3)
Stock 𝑖’s strategy is then defined to be the 𝑆 with the largest 𝑆𝑃𝑖,𝑆,𝑡 in month 𝑡.
Table 2 presents three Strategy Profile examples. Stock AAA’s Social Considerations Strategy Profile weight is the largest at 27.8% and becomes its designated strategy for the month. Its weights sum to 100%, with no weighting for the Economic Conditions, Market Conditions, and Opportunity strategies. Similarly, BBB is designated a Future Growth Stock while CCC is an Opportunity stock. Untabulated results show that stocks remain in a particular strategy pool for an average of 14 months, with Market Conditions the least stable and Valuation the most stable pools. Thus, strategy stock pools move about the equity universe as the result of fund manager buy and sell decisions in response to an ever changing economic and market environment. The Competitive Position, Future Growth, and Valuation pools are the largest since the number of funds in the corresponding fund strategies are also the largest.
A fund’s Consistency in a particular month is based on the percent, by count, of own-strategy stocks held by the fund. For example, if a fund holds fifty different stocks, 10 of which belong to its strategy, the fund’s percent is 20%. This percent is converted into a standard normal deviate, based on the distribution of the percent of own strategy stocks held by each fund in the strategy, truncated at three standard deviations above and below the mean, rescaled to range between 0 to 10, and then rounded to the nearest whole number (0 through 10). Consistency is based on the number of distinct stocks, ignoring weights of stock, to empirically distinguish the notion of strategy consistency with that of conviction, which is described in the next section and based on the degree to which managers weight their top positions.
2.2.3. Activeness and conviction
Table 3: Vanguard Funds used as Fund Benchmarks
This table lists the name, ticker symbol, and inception date of the nine Vanguard index funds used as benchmarks in this paper, along with their size and style dimensions, which are listed in the row and column headings, respectively.




2.3. Summary statistics and strategy validation



3. Main results
3.1 Consistency and fund performance

where the slopes are estimated via Eq. (7) over the same preceding 36 months (𝑇 − 36, … , 𝑇 − 1) as those used to identify the best-fit Vanguard benchmarks and rolling correlations in Section 2.




4. Conclusion
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ACKNOWLEDGMENTS
This paper would not have been possible without the support and infrastructure development provided by Andy Howard, Joel Coppin, Lambert Bunker, Dave Stock, and Andrew Detzel. Any remaining errors are my responsibility.
C. Thomas Howard, PhD, is professor emeritus at the Reiman School of Finance, Daniels College of Business, University of Denver and chief executive officer and chief investment officer at AthenaInvest, Inc. Contact him at This email address is being protected from spambots. You need JavaScript enabled to view it.
ENDNOTES
- See., e.g., Jensen (1968), Malkiel (1995), Carhart (1997), Fama and French (2010), and Ferreira, Keswani, Miguel, and Ramos (2013). Several studies find, however, that managers select stocks that outperform benchmarks before expenses. See., e.g., Grinblatt and Titman (1989, 1993), Daniel, Grinblatt, Titman, and Wermers (1997), Chen, Jegadeesh, and Wermers (2000), Wermers (2000), Alexander, Cici, and Gibson (2007), Berk and van Binsbergen (2015), and Antón, Cohen, and Polk, (2021).
- AthenaInvest.com
- Unlike many propriety measures in the literature, Athena’s consistency measure, along with other strategy information, is available to researchers, at no cost to them, who are willing to sign a non-disclosure agreement.
- The principal investment strategy has been required by the SEC since 1998.
- Chan, Dimmock, and Lakonishok (2009) also show commonly used size and value/growth-based styles are insufficient to capture cross-sectional variation in common fund strategies and therefore are inadequate to base benchmarks on. In an earlier paper, Howard (2010), using a cross-correlation analysis similar to the one described above, finds that self-declared strategy is much more effective than is the Morningstar style grid in forming fund clusters that pursue the same return factors. Even more surprising, he finds that random clustering outperforms style grid clustering in this regard. Among the three approaches, the style grid produces the worst clustering results.
- https://indexcalculator.ftserussell.com
- https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
- Their analysis used over 54,000 prospectuses and is available for the vast majority of U.S. equity mutual funds. About 7% of funds cannot be strategy identified because a prospectus cannot be located, or the prospectus did not provide useful strategy information (such a prospectus might simply state that the goal of the fund is to earn superior returns). While the Athena strategy is economically motivated, Abis and Lines (2022) use machine learning to make a statistical strategy identification also based on prospectus principal investment strategies and find that funds actions generally line up with their stated strategies.
- To be clear, they restrict the size of funds when assigning strategies to stocks, but we assign strategies and define strategy consistency for all funds regardless of size.
- Berk and van Binsbergen (2015) also include two international Vanguard funds, but we do not since we only consider the performance of U.S. equity funds.
- Amihud and Goyenko use the R^2 from the regression of fund returns on the Fama-French-Carhart four-factor model although the intuition is based on how closely a fund tracks its benchmark. We form R^2 relative to the best-fit benchmark following the intuition, but untabulated results show that results in this paper are robust to using the R^2 based on the Fama-French-Carhart model.
- For calculating the cap relative weight, we employ the Antón et. Al. (2020) approach of dividing a stock’s market capitalization by the sum of the market capitalization of all stocks held in the fund’s portfolio.
- Two managers following very different strategies could have high correlations purely because they have very similar market betas, and vice versa. Hence, we remove the common variation driven by the market to try to isolate excess correlation driven by strategy. However, it is important to note that managers picking similar stocks will also have similar betas so correlations in market-adjusted returns will understate own-strategy correlation.
- Cremers et al (2013) also propose a seven-factor model with mid-cap factors and separate value-minus-growth factors for the three size groups but find the four-factor model has lower tracking-error volatility when using monthly data as opposed to daily data. Untabulated results show that all inferences in this paper are unchanged using the Fama-French-Carhart model instead of the Cremers et al. (2013) model. Huij and Verbeek (2009) also show that using factors that ignore implementation costs, such as those in the Fama-French-Carhart model, biases fund performance results.
- Consistency takes discrete values 1 through 10, so we define consistency portfolio 1 (Low) consists of funds with consistency of 1 or 2 and portfolio 5 (High) consists of funds with consistency of 9 or 10. Portfolios 2 through 4 are defined similarly. Activeness and Conviction are continuous, so for these variables, we define portfolio 1 (Low) through 5 (High) by simple quintiles.
- Results are similar if instead of estimating Eq. (7) over the whole sample for the five portfolios, we instead use the time-series average of the monthly portfolio-level average fund alphas estimated via Eq. (8).
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