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Equity Perspectives

Historical returns should be an element in manager hiring decisions—but it’s important to understand context.

There is little doubt that Warren Buffet has been a successful investor, perhaps the best in our generation.  Chart 1 depicts the rolling one-year excess returns of Buffett’s investment company, Berkshire Hathaway, over the S&P 500, over three and one-half decades through early 2016. Under Buffett’s leadership, Berkshire produced extraordinary excess returns over the past 35 years, but there were number of periods in which the Berkshire investment portfolio underperformed significantly, at times for several years in a row.

Suppose we were to remove the Berkshire Hathaway label from the chart and present it as the handiwork of an unnamed investment manager. How many of those in charge of invesment policy criteria would, in 2000 or 2011, reject this unnamed manager’s portfolio as a new plan option?  And would fiduciaries evaluating their existing lineups retain this manager during these periods?

 

Chart 1. Berkshire Hathaway’s One-Year Excess Return versus U.S. Equities


Source: Newfound Research, based on Yahoo Finance data. The chart shows the excess return of Berskshire Hathaway stock versus the S&P 500® Index, from March 1980 to February 2016.  Performance data reflect total returns and include the reinvestment of dividends. 
Past performance is no guarantee of future results. The historical performance of each asset class is provided to illustrate market trends; there is no assurance that past trends will continue into the future. For illustrative purposes only and does not reflect any specific portfolio managed by Lord Abbett or any particular investment. Indexes are unmanaged, do not reflect the deduction of fees or expenses, and are not available for direct investment.


There’s not much debate over whether Buffett actually is a successful investor; we have the luxury of a lot of data to prove this.  But what the Berkshire chart shows is that even the most successful long-term managers may experience periods of underperformance—sometimes lengthy—through the years.  

Fiduciaries, in their role as manager evaluators, often don’t have decades of performance data at their disposal, and thus must use recent performance figures as a next best option.  In addition, they may not have the tools, time, or resources for further qualitative diligence.  So, operating under uncertainty, a fiduciary periodically checks a manager’s returns and makes judgments about whether the fund’s investment process is working.  One problem with this approach is the potential for the evaluator to be overconfident in his or her interpretations based on a limited dataset.

Overconfidence—one of the most thoroughly documented behavioral biases—is particularly straightforward to demonstrate in performance evaluation.  As an example, consider how many years a fiduciary needs to be 95% sure that a manager has sufficient skill to produce benchmark outperformance over the long haul.  Many assume, and consequently codify in investment policy criteria, that a few years of consistent outperformance or underperformance can give this level of certainty in a manager’s future performance potential.  Is that a correct assumption?

To test that, let’s turn to a widely followed measure of risk-adjusted return, the information ratio.1 Let’s posit that the manager under review has a stellar information ratio of 0.5, which would be mean something like 2% outperformance, with a 4% tracking error.2  By way of comparison, Berkshire Hathaway, between 1976 and 2011, had a 0.56 information ratio for its public stock holdings.  So, yes, a 0.5 information ratio is good.  Even with a 0.5 information ratio, though, assuming alpha3 is normally distributed, we would need 16 years of performance history to be 95% sure that this is, indeed, an outperforming manager. Said another way, a fiduciary would need to hold this excellent hypothetical manager for 16 years to be 95% confident that the manager would produce excess returns.4  That is a much longer horizon than we normally see employed in fund evaluation.

Exacerbating this problem is that excess returns to managers are not an independent, normal distribution.  Rather, they tend to be cyclical because outperformance and underperformance, relative to a benchmark (particularly at extremes), are often due in large part to factor exposures.  For example, we now know that much of Buffett’s performance can be “explained” by his portfolio’s exposure to a particular set of factors: value, quality, and low volatility.  His stock selection that resulted in these factor exposures showed foresight, and was rewarded over an extended time period.  But during periods when these factors were out of favor, Berkshire Hathaway also underperformed, sometimes severely, as shown in Chart 1.

This illustrates why analysis of the factor influences for a strategy can provide helpful and appropriate context.  Understanding the factors at work can help fiduciaries stay allocated to managers with unique, proven processes through a period when the process is out of favor.

Value, for example, has been one of the more consistent excess return sources over time; but Chart 2 shows that investor preferences for value and growth go through somewhat regular cycles. (These cycles happen to line up well with Berkshire Hathaway’s excess returns.)  So, a good manager with a persistent value bias may outperform over time, but there likely will be multiyear periods where he or she significantly underperforms because of this bias.

 

Chart 2. Value’s Long-Term Outperformance Has Come with Some Hiccups

Source: Research Affiliates, using data from CRSP/Compustat. The chart depicts the rolling two-year premium of a representative value portfolio versus a growth portfolio.
Past performance is no guarantee of future results. The historical performance of each asset class is provided to illustrate market trends; there is no assurance that past trends will continue into the future. For illustrative purposes only and does not reflect any specific portfolio managed by Lord Abbett or any particular investment. Indexes are unmanaged, do not reflect the deduction of fees or expenses, and are not available for direct investment.

 

As cyclical as this type of factor performance is,  timing portfolio moves around these swings is very difficult.  Most market observers accept that major factors like the market factor and commodity prices are not reliably timeable.  Some, like Rob Arnott, CEO of Research Affiliates, argue that mean reversion in factor valuations can and should be used to vary exposures.  Research Affiliates recently illustrated (see Chart 3) the benefits of buying poor recent factor performance and selling strong recent factor performance: that is, practicing mean reversion versus performance chasing. 

 

Chart 3. Trend-Chasing versus Contrarian Strategies: Which Historically Has Outperformed?

Source: Research Affiliates, using data from CRSP/Compustat and Worldscope/Datastream. Data cover the period from January 1977–August  2016.
This chart displays the average annual alpha and Sharpe ratio5 of (1) an approach that buys the three best performing strategies each year, (2) the equally weighted blend of eight strategies or factors examined (two approaches to value, low beta, gross profitability, momentum, size, illiquidity, and investment), and (3) a contrarian approach that buys the three worst-performing strategies, also based on a blend of one-, three-, five-, and 10-year performance.
Past performance is no guarantee of future results. The historical performance of each asset class is provided to illustrate market trends; there is no assurance that past trends will continue into the future. For illustrative purposes only and does not reflect any specific portfolio managed by Lord Abbett or any particular investment. Indexes are unmanaged, do not reflect the deduction of fees or expenses, and are not available for direct investment. Diversification does not guarantee a profit or protect against a loss.

 

Others, like Cliff Asness at AQR Capital Management, hold that timing should be attempted only at historical extremes, and even then with caution.6  Even if mean reversion at extremes is somewhat reliable, investors tend to get it backwards, by not practicing mean reversion and instead chasing recent performance.  This can be disastrous to a portfolio.

Investor response to the cycle of factor performance—selling when underperforming and buying when outperforming—is what creates opportunity for long-term outperformance through consistent exposure to these factors.  Often these pro-cyclical sales and purchases are due to a misattribution of recent results to a skill component, positive or negative, when much of the variance in fund performance is actually due to the cycle of factor returns.

Table 1—taken from  a recent paper by Bradford Cornell, Jason C. Hsu, and David Nanigian, titled “The Harm in Selecting Funds That Have Recently Outperformed”—summarizes the returns from buying top-decile mutual funds, holding them for three years, and then rotating to the new top-decile funds (the “winner strategy”), compared with that same strategy for bottom-decile funds (the “loser strategy”) and middle-decile funds (the “median strategy”).  The underperformance of the top-decile strategy is dramatic compared with the bottom-decile strategy, suggesting there is mean reversion to fund returns over this holding period.

 

Table 1. Winner, Median, and Loser Performance (36-Month Evaluation and Holding Periods)

Source: Bradford Cornell, Jason C. Hsu, and David Nanigian, “The Harm in Selecting Funds That Have Recently Outperformed.” Input data (via Morningstar Direct) from January 1994 to December 2015.
1The capital asset pricing model (CAPM) is a model that describes the relationship between systematic risk and expected return for assets, particularly stocks. CAPM is widely used throughout finance for the pricing of risky securities, generating expected returns for assets given the risk of those assets and calculating costs of capital. See footnote 4 at the end of this article for additional information on the t-stat.
2The Carhart Four-Factor Model, an equity valuation model, expands on the Fama-French Three Factor Model (which incorporates market risk, size, and value) by adding one additional factor, momentum. See footnote 4 at the end of this article for additional information on the t-stat.
Past performance is no guarantee of future results. The historical performance of each asset class is provided to illustrate market trends; there is no assurance that past trends will continue into the future. For illustrative purposes only and does not reflect any specific portfolio managed by Lord Abbett or any particular investment.

 

What’s troubling about the previous study is that the top-decile selection methodology is actually not far from the approach used by some plan sponsor firms and their consultants.  Another oft-cited paper, “The Selection and Termination of Investment Management Firms by Plan Sponsors,” by Amit Goyal and Sunil Wahal, examines this plan sponsor behavior directly.  Using hiring and firing data (1994–2003) from 3,400 plan sponsors, the researchers found that the fiduciaries hired investment managers after large recent positive excess returns, and fired managers with negative excess returns (see Chart 4).  Further, the hired mangers underperformed the fired managers over every measurement period, from one to three years, after the hiring/firing decision. 

 

Chart 4. Examing the Aftermath of Plan Sponsors’ Hiring/Firing Decisions

Source: Amit Goyal and Sunil Wahal, “The Selection and Termination of Investment Management Firms by Plan Sponsors.” The chart uses hiring and firing data from 3,400 plan sponsors and covers the 1994–2003 period.
Past performance is no guarantee of future results. The historical data are for illustrative purposes only, and do not reflect any specific portfolio managed by Lord Abbett or any particular investment..

 

An investment manger can avoid performance chasing and more efficiently reap the rewards of risk factor exposure by adhering to well-considered strategic allocations.  In addition, if a manager is underperforming in times when you would expect underperformance, when their style is out of favor, it is often a mistake to attribute this underperformance to a lack of skill.  The mistake is rooted in overconfidence in the interpretations of a limited history.  We suggest that it is much more important to look at how a manager achieved past returns.  Risk factors and modern portfolio theory statistics, such as information ratio, can help in this regard.  Longer-term records that show historical performance in a variety of markets also can help identify exposures that may be driving extreme short-term performance.  A careful analysis of a manager’s performance over a longer time horizon can provide important context for fiduciaries making critical hiring—and firing—decisions.

 

1The information ratio is a measure of the risk-adjusted return of a financial security (or asset or portfolio). It is defined as expected active return divided by tracking error, where active return is the difference between the return of the security and the return of a selected benchmark index, and tracking error is the standard deviation of the active return.

2Tracking error is a measure of the risk in an investment portfolio that is due to active management decisions made by the portfolio manager; it indicates how closely a portfolio follows the index to which it is benchmarked. The best measure is the standard deviation of the difference between the portfolio and index returns.

3Alpha is a measure of performance on a risk-adjusted basis. Alpha takes the volatility (price risk) of a mutual fund and compares its risk-adjusted performance to a benchmark index. The excess return of the fund relative to the return of the benchmark index is a fund's alpha.

4 A t-test is an analysis of two populations means through the use of statistical examination. A t-test looks at the t-statistic, the t-distribution and degrees of freedom to determine the probability of difference between populations. In this case, the information ratio can be compared to the t-statistic since there is a connection between the statistical significance of excess returns and statistical significance of an information ratio.  The t-statistic has a t distribution with T - 1 degrees of freedom where T is the number of time periods. This statistic is based on a hypothesis test and the result of this test can be determined by standard t-tables. When testing the statistical significance of the information ratio, one would choose a hypothesis test where the null hypothesis would be that the excess returns over the benchmark portfolio would be zero and the alternative hypothesis would be that the excess returns are positive.

5The Sharpe ratio was developed by Nobel laureate William F. Sharpe as a measure of risk-adjusted performance. It is calculated by taking an asset class’s (or portfolio’s) excess return above the risk-free rate and dividing it by the standard deviation of its returns. The greater the Sharpe ratio, the better the risk-adjusted performance has been.

5Cliff Asness, “How Can a Strategy Still Work If Everyone Knows About It?” AQR.com, August 31, 2015.

 

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