Active mutual funds’ performance has been of immense interest to financial economists for a very long time (Jensen, 1968; Ferson and Schadt, 1996; Carhart, 1997; Kent, Grinblatt, Titman, and Wermers, 1997; Wermers, 2000; Pástor and Stambaugh, 2002; Cohen, Coval and Pástor, 2005; Fama and French 2010; Kacperczyk, Nieuwerburgh and Veldkamp, 2014; Jensen, Fisher and Tkac, 2015; Berk and Van Binsbergen, 2015).
Petajisto (2013) grouped equity mutual funds according to how active their managers are and finds that the most active fund managers are able to outperform their respective benchmarks even after fees. These findings are contrary to the numerous studies on active fund management that found that active managers tend to consistently underperform the market (Jensen, 1968, McDonald, 1974, Sharpe, 1975, Gruber, 1996, Wermers, 2000, Mibiola, 2013).
Apart from a fund manager’s skill in spotting investment opportunities, the ability to outperform a benchmark inter alia, is also dependent on the returns to scale which also interact with skill: for example, a small fund which is less skilled can outperform a large fund which is more skilled (Pástor et al., 2015).
There are two hypotheses on the nature of returns to scale in active management both motivated by liquidity constraints (Pástor et al., 2015). First is a decreasing returns to scale at fund-level i.e. the ability of an active fund to outperform its benchmark decreases as the size of the fund increases (Perold and Solomon, 1991, and Berk and Green, 2004). There is also decreasing returns to scale at the industry level i.e. as the ability of any particular fund to outperform a given benchmark dwindles as the size of the industry of active mutual funds increases (Pástor and Stambaugh, 2012).
Asset prices are affected by large trades by large funds’ this will have an impact on the performance of such funds an also, as the number of funds seeking to outperform a given benchmark increases, there is an incremental amount of monies seeking the chance to outperform which leads to prices increases thereby making such a venture elusive. There is increasing amount of evidence that mutual fund trading activities can influence prices on the stock market. Stock market returns are influenced by the aggregate flows into equity mutual funds (Edelen and Warner, 2001). Edelen, Evans, and Kadlec (2007) further posit that another major source of diseconomies of scale for mutual funds can be attributed to trading cost.
Page | 121 Hypothesis on returns to scale at the fund level have been tested in a number of studies with inconclusive findings (Chen, Hong, Huang, and Kubik, 2004; Pollet and Mungo, 2008; Yan, 2008; Ferreira, Keswani, Miguel, Ramos, 2013, a,b;. Reuter and Zitzewitz, 2013).
Cremers and Petajisto (2009) presented a new measure of active portfolio management –Active Share – which depicts the share of portfolio holdings that diverge from the holdings of the benchmark index. The study reports that Active Share forecasts fund performance this implies that funds that have the highest Active Share substantially outperform their benchmarks before and after expenses, and exhibit strong performance persistence as well. Petajisto (2013) further reports that even after fees, funds with the highest Active Share outperformed their benchmarks.
Figure 6.4: The Different Kinds of Active Share
Source: Adopted from Cremers and Petajisto (2009), cited in Petajisto (2013)
Figure 6.4 illustrates the two aspects of active management and how each can be associated with different styles of active management. Diversified stock pickers display low tracking error and high Active Share, the opposite is true for funds that concentrate on factor bets.
Concentrated funds apply both stock selection and factor bets, and therefore score high on each
Page | 122 measure. Closet indexers however score low on each measure. Closet indexers claim to be active managers but are far less involved in any kind of active management.
In the study, Petajisto (2013) distinguishes different kinds of active management explaining that active managers can only add value by differing from the benchmark index either through factor timing (also referred to as tactical asset allocation) which entails time-varying bets on broad factor portfolios such as being overweight in a particular sector of the economy or equity selection which entails active bets on single equities such as choosing just one equity from a given industry.
Figure 6.5: The Evolution of Active Share in the US (1980 to 2009)
Source: Petajisto (2013: 81)
Figure 6.5 illustrates the portion of assets in United States mutual funds in the five categories of Active Share from 1980–2009. The group of funds at the bottom, that have Active Share less than 20%, are pure index funds which have grown from almost zero in 1980 to around 20% of mutual funds AUM in 2009. The next two category of funds that have Active Share
Page | 123 ranging from 20% and 60%, and are closet indexers and have become even more popular than pure index funds and accounted for about one-third of mutual funds AUM in 2009.
Petajisto (2013) reports that stock selection as depicted by a high Active Share is compensated for in the equity market with the most aggressive stock pickers adding value even after fees for their investors. On the other hand, factor bets, represented by high tracking error on average, did not add value for their investors. Table 6.1 presents the equal-weighted returns for the five types of funds, and the averages across all groups. The figures in parenthesis are the t-statistics.
Table 6.1: Performance of Active Share
Source: Petajisto (2013: 84)
However, Frazzini, Friedman and Pomorski (2015) of AQR Capital Management LLC, investigated the Active Share, a measure using the same data set as Cremers and Petajisto (2009) and Petajisto (2013) and concluded that the empirical support for the Active Share is weak and is completely influenced by the strong correlation between the benchmark type and Active Share. (Frazzini et al., 2015) further argue that Active Share rather correlates with the returns of the benchmarks, but cannot predict actual fund returns and conclude that there is no theoretical or empirical justification that Active Share may improve investors’ returns.
Page | 124 Rebutting the arguments of Frazzini et al. (2015), Cremers (2015) argues that the AQR paper
“falls into a wonderfully creative but altogether different genre, which we label the Wonderland Genre” and should therefore not be interpreted by applying typical academic standards and further arguing that AQR paper is employing a “Sentence First, Verdict Later.” Style and their results cannot be taken at face value, because the information is not shared thereby reversing their main conclusion. In conclusion, Cremers (2015) points out that AQR funds tend to have low Active Shares and little outperformance and hence appearing fairly expensive considering the amount of differentiation offered by the firm.
Figure 6.6: Performance of Active Managers over the Long-Run
Source: Allianz (2014: 5)
According to Allianz Global Investors, the global active equity managers have been able to generate significant value for their clients over the past thirty years, according to Mercer’s GIMD database although recently, there have been a significant slowdown of the pace of outperformance hence calling for the need for more active management (Allianz, 2014). Figure 6.6 shows how over the long-term, the median active fund outperforms the MSCI index and also had a relatively minimal slump during the recent Global Financial Crisis.
Page | 125 Chlanger, Philips and LaBarge (2012) advise investors not use only active share as their sole metric in selecting a portfolio but rather include it in their toolkit in selecting portfolios.
Schlanger et al. (2012) conclude that higher active share do not predict outperformance, do not significantly outperform portfolios of lower active shares and had larger dispersion of excess returns. Higher the active-share resulted in higher the fund costs. They also found that funds that had the highest active share were concentrated in small-cap and mid-cap equities.
Figure 6.7: The Relationship between Fund Age and Performance (March 1993 to December 2011)
Source: Pástor, Stambaugh and Taylor (2015: 35)
Cohen, Leite, Darby and Browder (2014) argue that although active share may assist investors in comparing active managers, it lacks consistency as a measure across different market capitalization, size, benchmarks and mandates. Higher active share comes with a greater level of return dispersion as well as a higher downside risks and strikingly, for large-cap managers, the relationship between excess return and a higher level of active share appears to be largely due to exposures to smaller-cap portfolios (Cohen et al., 2014).
Pástor, Stambaugh and Taylor, (2015) empirically analysed the characteristics of returns to scale in active mutual fund management and reported compelling evidence at the industry level of decreasing returns stating that there is a negative correlation between the active mutual fund industry size and the ability of funds to outperform passive benchmarks. Pástor et al. (2015)
Page | 126 further report that over time, the industry of active fund management has become more skilled with the upward trend in skill coinciding with industry growth and precluding the skill improvement from enhancing fund performance. Their study also reveals a deterioration of performance over the lifetime of a typical fund with the correlation being stronger for funds with relatively higher volatility, turnover and small-cap funds citing decreasing returns to scale at the industry-level as an explanation for this phenomenon. Figure 6.7 illustrate the relationship between performance and fund age. The average fund’s performance declines over its lifetime. This result is almost monotonous for funds with that have existed for the past 12 years over the period of the study. According to Pastor et al (2015), the point estimators for the age fixed effects diminish in an approximately linear style from 0.37% every month at age one to 0% at age 12, from this point, they become almost flat therefore, as funds age, their performance diminish.
Philips, Kinniry and Todd (2014) propose combining both active and passive funds in a portfolio as this strategy minimises underperformance, downside risk and dispersion of returns but manage to produce a positive excess returns over the period albeit lower than would have been the case with portfolio made up of only actively managed funds. Figure 6.8 comprises all diversified top quintile U.S. equity funds from 2004 to 2008. Figure 6.9 is made up of the same funds as Figure 6.6, however, it combines each fund in a 50/50 ratio, with a passive index that matches the fund’s style of investment. Index returns are reduced by 10 basis points per year to reflect implementation expenses and excess returns are measured according to the stated benchmark of the fund.
The data reflect excess returns from 2009 to 2013 for the 1,205 funds that were in the top quintile from 2004 to 2008.
The debate between active and passive management still continues and in recent times it appears the argument for passive management is gaining traction. In the past few years, investors have gravitated towards less expensive and passively managed ETFs and index funds particularly in the United States and poor performance of actively managed funds against their benchmarks has been cited as the reason for this trend (Lamy and Strauts, 2015). Figure 6.10 illustrates the net flow for index and non-index funds in 2014.
Page | 127 Figure 6.8: Distribution of excess returns for the five years ended 2013 for top quintile funds as of 2008
Source: Philips, Kinniry and Todd (2014: 3)
Figure 6.9: Same distribution but including 50% apportionment to the style benchmark of each fund
Source: Philips, Kinniry and Todd (2014: 3)
Page | 128 As of December 2014, 382 index funds had net assets of $2.1 trillion while demand for index funds continued to increase, with $148 billion in net new cash flow. In 2014, among households that owned mutual funds in the United States, 31% had at least one equity index fund (Investment Company Institute, 2015).
Figure 6.10: Net Flows for Index and Nonindex Funds by Region (2014)
Source: Lamy and Strauts (2015: 9)