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Equity Perspectives

Why should anyone care what portion of a manager’s returns come from stock selection?

 

In Brief

  • The demand for high tracking error, “satellite” equity strategies is growing. However, these strategies often come with risk-factor exposures that may outweigh any edge gained from stock selection.
  • We believe there are three reasons why investors should be aware of the portion of a manager’s returns derived from stock selection versus factor exposure:

    –Factor exposures are no longer worth active management fees.

    –Active managers are better at stock selection than they are at factor timing.

    –Stock selection is more diversifiable than factor exposures.

  • The key takeaway: Strategies that focus on diversifiable, repeatable, stock-specific risk may offer a multi-manager platform more consistent risk-adjusted returns.

 

The recent landmark U.S. Department of Labor ruling on fiduciary standards is just the latest example of a pervasive trend of increasing scrutiny applied to allocators’ portfolio choices. In many cases, the scrutiny and risk of legal consequences is motivating a shift to passive and index-based investment strategies. But in a post–quantitative easing, low-return environment for most asset classes, the need for active return has, arguably, never been greater. To balance these needs, many allocators have turned to a barbell approach, allocating very little active risk to the usually passive core portion of a typical core-satellite portfolio—and a lot of active risk to the satellite portion. Active managers, in turn, have adopted an “active at all costs” approach to fit this model, targeting high tracking error as a necessary strategy differentiator.

We think both the passive and active sides of a barbell strategy have merit for many investors. But confining active management to a high-risk functionality limits its reach and effectiveness. Often, the intention of high tracking error managers is to isolate the impact of research and stock-selection efforts by not confining a manager’s choices to index constituents and by allowing heavy concentrations in individual stocks, sectors, and styles. While well-intentioned, this approach to portfolio construction can lead to uncompensated risks.

Specifically, the lack of diversification in “conviction” portfolios gives rise to risk-factor exposures that may outweigh any edge gained from security selection. Chart 1 shows this counterintuitive phenomenon.1 As the funds depicted in the chart exhibit more tracking error, less of that tracking error comes from stock selection.

 

Chart 1. Sources of Variance among the Largest Mid- and Large-Cap Value Funds

Source: Morningstar, Inc., Axioma, Lipper, and Lord Abbett. As of December 31, 2015. Data points represent values for the 10 largest mutual funds in two Lipper categories: Mid-Cap Value and Large-Cap Value. For illustrative purposes only and does not represent any portfolio managed by Lord Abbett or any particular investment.

 

Why should anyone care what portion of a manager’s returns come from stock selection? We believe there are three reasons:

1) Factor exposures are no longer worth active management fees.

This is a relatively new phenomenon. Twenty years ago, all but the most sophisticated investors needed active managers to gain efficient and consistent exposure to various investment styles like value, quality, and momentum. The emergence of smart beta strategies has changed that. While smart beta funds usually don’t offer anything new to the investment world, what they do bring to the table is targeted exposure to return sources, and they can do it cheaply. Because their strategies are rules-based, smart beta managers don’t have to employ researchers or analysts and can pass those savings on to investors in the form of low management fees.

As smart beta adoption expands, exposure to factor returns becomes a commodity, a fortuitous development for fee-conscious investors and allocators. We believe active management’s role needs to shift to providing returns that cannot be replicated by smart beta factor exposures. In a meaningful way, exposure to factor returns is no longer an “active” exposure, and the market for active management is adjusting to this new paradigm. This is illustrated in Chart 2, which shows how active manager fees decline as more of the active variance is explained by factors.2

 

Chart 2. Actual Fees and Predicted Fees versus the Fraction of Managers’ Active Variance (R2), January 2010–December 2014

Source: Ronald N. Kahn and Michael Lemmon, “The Asset Manager’s Dilemma: How Smart Beta Is Disrupting the Investment Management Industry,” Financial Analysts Journal (January–February 2016). The multifactor model referenced in the chart is derived from estimated time-series regressions on six smart beta factors: the market, small size, value, quality, momentum, and low volatility. The R2 from these regressions is a measure of the fraction of managers’ active return variance that can be explained by smart beta factors. The chart shows that active manager fees decline as more of the active variance is explained by factors; actively managed funds in the highlighted area, with higher fees for a similar level of factor exposure to those below the predicted fee line, may face pressure on their fees.

 

2) Managers are better at stock selection than they are at factor timing.

For most investors, this statement is intuitive. It is well known that experts’ predictions of macro factors such as the price of oil or interest rates are rarely correct. The most compelling empirical proof that stock selection is a more reliable source of returns than factor betting is the active share literature pioneered by K.J. Martijn Cremers and Antti Petajisto.3 Sorting managers on relative active share and tracking error allowed the researchers to approximate skill in stock selection (an activity associated with higher active share) versus skill in factor betting (an activity associated with higher tracking error). The group of managers with higher active share and lower tracking error, which the researchers called diversified stock pickers, outperformed the other manager groups by a significant margin. Cremers and Petajisto noted that “most active stock pickers have enough skill to outperform their benchmarks even after fees and transaction costs. In contrast, funds focusing on factor bets seem to have zero to negative skill, which leads to particularly bad performance after fees.”

Paradoxically, the active share literature has been used to justify a “more is better” approach with regard to activeness, when the researcher’s findings were actually more nuanced, finding that a certain kind of activity (stock selection) was beneficial while another kind (factor betting) was not. Cremers and Petajisto are not the only researchers to confirm this finding. (We will offer a comprehensive review of research supporting the superiority of diversified stock picking as a source of alpha in our upcoming white paper, “The Uncommon Virtues of Security Selection.”)

3) Stock selection is more diversifiable than factor exposures.

This is true at a portfolio level, and when combining portfolios in a multi-manager structure. At the portfolio level, stock selection can offer thousands of independent chances to apply an investment team’s skill, compared to just a few opportunities for a factor-timing manager. The point of factor identification is to label common determinants of large portions of security returns. So factor bets, by their nature, are powerful bets that are hard to diversify. Again, empirical evidence supports the notion that lower tracking-error strategies typified by diversified stock pickers generate more efficient excess returns than higher tracking error approaches typified by large factor bets. 

At the multi-manager level, common factor loadings do not diversify, while idiosyncratic risk does. So, combining portfolios with large weights on common factor risks will not yield much in the way of diversification benefits. Combining portfolios with different factor risks can be more successful, as distinct factor correlations are often very low over multi-decade return horizons. However, over shorter horizons, such as the one-year rolling time periods shown in Chart 3, factor correlations can be erratic.4 Excess returns to factor exposures are similarly erratic, meaning allocators who rely on manager factor exposures for outperformance must be prepared to endure extended and sometimes simultaneous periods of underperformance.

 

Chart 3. Value and Momentum Risk Premia: Rolling One-Year Correlation Based on Monthly Data, December 1927–March 2016

Source: Kenneth R. French Data Library, mba.tuck.dartmouth.edu. For illustrative purposes only and does not represent any portfolio managed by Lord Abbett or any particular investment. Past performance is not a reliable indicator or a guarantee of future results.

 

The Implications for Allocators

Without a multi-decade evaluation horizon, an allocator may well abandon higher-risk managers during times of underperformance. Indeed, this occurs frequently. In a 2008 study of plan sponsors’ hiring and firing decisions over 20 years, researchers Amit Goyal and Sunil Wahal found that in the years after the decision, recently fired managers produced relatively higher excess returns compared to managers who were hired.5

Those results underscore the uncertainty associated with higher tracking-error strategies, limiting the usefulness of these approaches to an allocator. Managers with moderate active risk—typified by well-diversified stock pickers—can offer more consistent returns, helping to mitigate this uncertainty and, consequently, to minimize suboptimal hiring and firing decisions.

The idiosyncratic stock selection performed by diversified stock pickers constitutes true activeness in a world where factor exposures are commoditized. The research we’ve examined here suggests that a strategy focusing on diversifiable, repeatable, stock-specific risk may offer a multi-manager platform more consistent and diversifiable returns over the long term.

 

1Morningstar, Inc., Axioma, and Lord Abbett.
2Ronald N. Kahn and Michael Lemmon, “The Asset Manager’s Dilemma: How Smart Beta Is Disrupting the Investment Management Industry,” Financial Analysts Journal (January–February 2016).
3K.J. Martijn Cremers and Antti Petajisto, “How Active Is Your Fund Manager? A New Measure That Predicts Performance,” The Society for Financial Studies, 2009.
4Kenneth R. French Data Library, mba.tuck.dartmouth.edu.
5Amit Goyal and Sunil Wahal,” The Selection and Termination of Investment Management Firms by Plan Sponsors,” The Journal of Finance (August 2008).

 

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