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Equity Perspectives

Recent research shows that stock pickers historically have been more likely to outperform their benchmarks through market ups and downs. 


Summary

  • Some investors believe that the stock market is so efficient that active management is of little value.

  • But market efficiency implies that the market distinguishes good stocks from bad on the basis of fundamentals. This suggests that the market will reward at least some active managers.

  • Which type of active management is most likely to be rewarded? Stock picking. Funds that are highly active in selecting stocks, while also refraining from large bets on sectors and other factors, beat their benchmarks by 1.39% annually between 1990 and 2009, after expenses.1

  • These funds have maintained their advantage even in highly correlated markets, outperforming their benchmarks by 0.97% annually from January 2008 through December 2009, after expenses.2

  • How have stock pickers managed to do this? By tapping into company-specific sources of return, something other types of active managers do to a lesser degree or not at all.


The stock market is so efficient, say some analysts, that it makes active management, especially stock picking, a poor strategy. Better, they say, to invest in index funds or exchange-traded funds and forgo active investments altogether.

Stock picking becomes even more futile, they believe, when stocks become highly correlated with each other, as often happens during periods of political and economic uncertainty. High correlations often mean that systemic or macro-level factors, such as political and economic events, are the forces behind stock price movements. If prices are responding to something other than fundamentals, then selecting equities on that basis would certainly appear to be ineffective.

So how viable is stock picking as an investment strategy? Walter Prahl, Lord Abbett Partner & Director of Quantitative Research, weighs in on the issue.

Does Market Efficiency Make Active Management Ineffective?
Those who argue that stock picking no longer works are really making a claim about the market’s efficiency, says Walter Prahl. An efficient market is one that makes price distinctions among different stocks on the basis of differing prospects for fundamental performance. "If the argument is that the stock market is losing the ability to do that," said Prahl, "then this is really an argument that efficiency is declining."

Such a decline is not out of the question. Economists Sanford Grossman and Joseph Stiglitz demonstrated more than 30 years ago that market efficiency is not a static condition but a dynamic process, Prahl noted.

The activity of active managers pushes the market toward greater and greater efficiency, until at some point the outperformance they expect to achieve falls enough so that they are no longer interested in being active. But a retreat by active investors, in turn, leads to a rise in inefficiency, which results in mispricings that attract them back into the market.

"If that dynamic process moves too far in one direction," added Prahl, "the self-interested actions of active investors will push it back—never to perfect efficiency, but to a degree of efficiency. Efficiency arises spontaneously out of the collective behavior of investors individually acting in their self-interest."

In fact, perfect efficiency is impossible. According to Grossman and Stiglitz, if a market were perfectly efficient, there would be no mispricings, and there would, therefore, be no incentive for investors to expend the effort required to be active.

So, in a normal market, active management is rewarded because stocks are sifted on the basis of their fundamentals. But what happens when stocks become highly correlated? In this case, prices are driven by macro forces, such as political developments and economic events, not fundamentals.

During the subprime and European debt crises, for example, it was rescue attempts by governments and central banks that caused markets to rise and fall. Those efforts resulted in "risk on/risk off" behavior, in which investors were shifting in and out of stocks, depending on the news.

In this kind of environment, it can appear that active management, and stock picking in particular, no longer works. But research shows that stock picking has, on average, historically outperformed other types of active management—even during periods when market correlations were high.

Active Management in Two Dimensions: Stock Picking and Factor Betting
To understand how stock pickers have been able to outperform even when stocks are highly correlated, it helps to know that there are two dimensions of equity fund management. One focuses on security selection—that is, picking potential winners and losers on the basis of company fundamentals, such as earnings, sales, growth, and valuation.

The other dimension focuses on "factors" that are believed to drive stock performance. These factors can include a company's sector, style (e.g., value versus growth), capitalization, or even momentum, based on a stock's recent performance. A factor can also be any characteristic that a fund manager believes will drive a stock price higher. A manager who believes that value will outperform growth, for example, would overweight those stocks that are classified as value stocks, regardless of company-specific fundamentals. Funds may overweight more than one factor, and some investors believe value and momentum, for example, work well together. Generally, this approach is known as "factor betting."

Few equity mutual funds focus on only one of the two dimensions. Most funds employ a combination of the two. And how far a fund leans in one direction or the other can be measured.

The stock-picking dimension is measured using a metric called Active Share.3 Developed by professors K.J. Martijn Cremers (formerly of Yale University) and Antti Petajisto (formerly of New York University), Active Share has gained broad acceptance in the asset management industry.4

Active Share is a concept that entails comparing the holdings and weightings of a fund in order to provide a numerical measure indicating how much a fund diverges from its benchmark. That is, it provides one indicator of just how active an active manager is.

An Active Share score can range from zero for a fund that matches its benchmark perfectly to 100 for one with no overlap whatsoever. Active Share can therefore be interpreted as the portion of a portfolio that differs from the benchmark.

To measure the second dimension, factor betting, Cremers and Petajisto use a common metric: tracking error.5 This makes intuitive sense. Tracking error measures how much a fund's performance diverges from its benchmark. So if a fund heavily overweights a sector such as energy, for example, then the fund is likely to diverge more than if it matched the benchmark's weighting.

Equity mutual funds can be plotted along these two dimensions. Chart 1 shows Active Share on the vertical axis and tracking error on the horizontal axis.

 

Chart 1. Active Management Consists of Two Dimensions

Active Share and tracking error measure stock picking and factor betting, respectively

Source: Lord Abbett.

 

Four Types of Actively Managed Funds
Using these two dimensions, Petajisto classifies actively managed funds into four groups.6 The first group consists of Stock Pickers (see Chart 2). This doesn't mean that other groups don't pick stocks, but that this group concentrates primarily on stock picking and less on factor betting. The group may even deliberately constrain weights on sectors and other factors remain close to the benchmark's weights. If a fund refrains from large factor bets, it should be more diversified and therefore less risky than funds that do not refrain.

 

Chart 2. Stock Pickers Score in the Top Quintile on Active Share

Source: Antti Petajisto, "Active Share and Mutual Fund Performance," Social Science Research Network, January 15, 2013.
For illustrative purposes only and does not represent any Lord Abbett mutual fund or any particular investment.

 

Factor Bettors make up the second group. They are highly active in weighting sectors or other factors. Many of these funds may also be fairly active in stock picking; their scores on Active Share can range widely (see Chart 3).

 

Chart 3. Factor Bettors Exhibit High Tracking Error

Most other funds make smaller bets on factors

Source: Antti Petajisto, "Active Share and Mutual Fund Performance," Social Science Research Network, January 15, 2013.
For illustrative purposes only and does not represent any Lord Abbett mutual fund or any particular investment.

 

Similar to Stock Pickers, the third group, Concentrated Managers, scores in the top quintile on Active Share, but unlike Stock Pickers, they also make large bets on factors. A fund might overweight certain sectors such as financials, for example, making the fund less diversified on this factor. The decision to overweight a factor may be intentional or may occur merely as a result of not constraining factor weightings to match those of the benchmark (see Chart 4).

Chart 4. Concentrated Managers Are Highly Active on Both Dimensions

Source: Antti Petajisto, "Active Share and Mutual Fund Performance," Social Science Research Network, January 15, 2013.
For illustrative purposes only and does not represent any Lord Abbett mutual fund or any particular investment.

 

A fourth group, Moderately Active Managers, falls in the midrange on both Active Share and tracking error. These funds' stock picks diverge less from the benchmark than the more active Stock Pickers, and they make fewer or smaller factor bets than Factor Bettors (see Chart 5).

 

Chart 5. Moderately Active Funds Score Lower on Both Dimensions

Source: Antti Petajisto, "Active Share and Mutual Fund Performance," Social Science Research Network, January 15, 2013.
For illustrative purposes only and does not represent any Lord Abbett mutual fund or any particular investment.

Of the Four Types, Which One Tends to Outperform?
Research on Active Share has demonstrated that of the four groups, only Stock Pickers, on average, beat their benchmarks during the 20-year period from 1990 to 2009.7 This doesn't mean top-notch Factor Bettors, for example, can't also outperform. But the Active Share research shows that of the four groups, only Stock Pickers have performed better, on average.

And the added returns are not insignificant. These funds beat their benchmarks by an annual average of 1.39% during this period, after expenses. (See Chart 6.) The other three forms of active management failed to best their benchmarks during this period. Concentrated Managers, for example, lagged by 0.89%, Moderately Active funds lagged by an average of 0.78%, and Factor Bettors missed by 2.19%.

 

Chart 6. On Average, Only Stock Pickers Beat Their Benchmarks

Average annual alpha, net of fees, 1990–2009

Source: Antti Petajisto, "Active Share and Mutual Fund Performance," Social Science Research Network, January 15, 2013.
For illustrative purposes only and does not represent any Lord Abbett mutual fund or any particular investment.

 

Stock Picking in a Volatile Market: Sailing into a Headwind?

So Stock Pickers, unlike the other three active management types, have beaten their benchmarks over the long term. But how have they done during periods of uncertainty, when stocks are highly correlated?

Petajisto's research shows that they outperformed their benchmarks even during the financial crisis of 2008–09, when intramarket correlations were extremely high.8 Stock Pickers, on average, exceeded their benchmarks by 0.97% (after expenses) during the period from January 2008 through December 2009.

How did they manage to do this? The answer lies in understanding the nature of stock returns.

Stock returns consist of two components, one systematic and the other nonsystematic, or company-specific. The former reflects macro effects, sector developments, regulatory changes, and the like—anything that can broadly affect all stocks. The latter reflects changes that affect an individual company.

"When the volatility of the first component increases, we see an increase in correlation among stocks," said Prahl. "But that doesn't mean that the nonsystematic component has gone away or that it has become smaller. It just means that the systematic component will be contributing a larger share to overall stock price movement than the nonsystematic component."

So, when the market becomes volatile, the contribution of the nonsystematic, or company-specific, component contributes less to performance, relative to the contribution from the systematic component. This contribution won't change in the absolute sense, but the ability of stock pickers to outperform will be smaller relative to overall market volatility, according to Prahl.

It's somewhat like piloting a steamship into a headwind. The thrust of the engine may not account for as much of the boat's movement as it would if the winds were calm, but even in a violent storm, it makes a difference. Likewise, when macro forces are buffeting a market index, Stock Pickers have historically been able to eke out some extra performance by capturing company-specific sources of return. The other three types of active management tap into that source to a lesser degree, or not at all.

"This is exactly what Petajisto discovered about fund managers during the credit crisis, when market correlations and volatility were high: the performance difference between high Active Share managers and low Active Share managers remained in the same range as its previous historical averages," Prahl explained, "despite the fact that overall market returns were much more volatile."

Stock picking has been a tried-and-true means of capturing alpha over time. The efficiency of the market ensures that stocks will be sorted on the basis of fundamentals, giving at least some active managers the opportunity to beat their benchmarks. Although there is no guarantee the trend will continue, research on Active Share demonstrates that Stock Pickers have historically been more likely than other types of active management to add alpha, even during periods of market volatility.

—Reported by Ron Vlieger

 

 

1 Antti Petajisto, "Active Share and Mutual Fund Performance," Social Science Research Network, January 15, 2013.
2 Ibid.
3 To calculate Active Share: Compute the absolute value of the difference between the weight of a stock in the portfolio — weight of that stock in the index. Do this for all stocks in the index and the portfolio. Sum these and divide the result by 2. 
4 K.J. Martijn Cremers and Antti Petajisto, "How Active Is Your Fund Manager? A New Measure That Predicts Performance," Yale Working Paper No. 06-14, March 31, 2009.
5 Tracking error (or more formally, tracking error volatility) is commonly defined as the time-series standard deviation of the divergence between a fund return and its benchmark index return. A typical active manager aims for an expected return higher than the benchmark index, but at the same time wants to have low tracking error (volatility) to minimize the risk of significantly underperforming the index.
6 Petajisto, op. cit.
7 Ibid.
8 Ibid.


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