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Practice What You Preach: Strategy Consistency and Mutual Fund Performance

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.

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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.

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