Rise of the Stock Pickers

Key Takeaways

  • While the market has struggled so far in 2022, the environment for stock picking has been favorable.
  • Historically, when value outperforms growth, more stocks outperform the overall benchmark. Such an environment provides a tailwind to stock picking.
  • Stocks are also moving independently of one another. We provide two measures to evaluate the degree of stock independence, both of which indicate stock performance is being driven to a large degree by idiosyncratic (stock-specific) concerns.
  • Investors are using management expectations to distinguish between stocks so far in 2022. During the Q1 earnings season, companies that beat their estimates saw a historically large premium relative to companies that missed earnings.

Introduction

2022 has not been a kind year for the equity markets. Through May 24, the S&P 500 is down 17.3% year-to-date, while the Nasdaq 100 is down 27.9%. The market’s struggles this year can be attributed to a newly hawkish Fed and inflation that has proven to be stickier-than-expected, which has impacted both consumer spending habits and bottom line earnings numbers for corporates. These headwinds have led to significant losses across the equity landscape, particularly in the technology, discretionary and communication services sectors.

In prior research, we have cited the unattractiveness of the equity market relative to fixed income securities[1]. While the backdrop for the equity markets remains challenging, this note will instead focus on the environment for stock-picking, which remains very favorable. Indeed, despite the poor performance of the market so far this year, it has been a good year to be a stock picker.

From a quantitative standpoint, we can frame the size of the stock picking opportunity. The fundamental law of active management says that a manager’s information ratio is driven by two factors: his/her skill (i.e. the ability to identify outperformers) and the breadth of the opportunity set (i.e the number of independent “bets” the active manager can make). In the following sections, we go through some analyses that frame the size of each of these drivers. In summary, while the market has struggled so far in 2022, the backdrop for stock picking continues to be quite favorable.

Sizing the Investment Opportunity Set

We start by considering the share of stocks outperforming the S&P 500. Irrespective of skill, stock pickers are likely to find more success in environments where more constituents outperform the overall benchmark. When more stocks outperform the benchmark, stock pickers have a higher likelihood of finding winners.

In Fig. 1 below, we show the share of S&P 500 constituents that outperform the overall benchmark during periods when growth outperforms value (left bar) and when value outperforms growth (right bar). From the chart, we see that when value outperforms, more stocks outperform the benchmark. In such environments, stock pickers enjoy a tailwind – even if they have zero inherent skill, on average, a stock picker is more likely to select a stock that outperforms the benchmark.

Fig. 1 – Share of Stocks Outperforming During Periods of Value and Growth Outperformance

Source: Bloomberg, Russell, S&P, Fundstrat analysis.Rise of the Stock Pickers
Note: Shows the share of S&P 500 stocks outperforming the benchmark (S&P 500 index) during months when the Russell 1000 Growth index outperforms (left bar) and underperforms (right bar) the Russell 1000 Value index. Period of analysis is from 1999 through April 2022. Transaction costs are not considered.

So far in 2022, value has outperformed growth (the Russell 1000 Value index has outperformed its Growth counterpart by 19% through May 24). In line with the analysis in Fig. 1 above, we have seen a larger number of stocks outperforming the benchmark so far this year, which has helped stock pickers.

Another factor affecting the success of stock picking is the breadth of the opportunity set to which stock pickers can apply their stock selection skills. For a stock picker with positive skill, the larger the breadth of independent investment opportunities available, the higher his/her information ratio (risk-adjusted return) should be. Note the keyword here, independent. If multiple opportunities exist, but are correlated to the same underlying driver, the breadth of investment opportunities is smaller than would appear.

One way to show the breadth of investment opportunities is by computing the pairwise correlation between stocks. In Fig. 2, we show the average pairwise correlation among S&P 500 constituents. When correlations are high, stocks tend to move more in tandem with each other, and are being driven by only a handful of underlying factors. When correlations are low, however, stocks tend to move more independently, and stock pickers can more easily distinguish between individual companies and their respective prospects for outperformance.

Fig. 2 – Pairwise Correlation of Individual Stocks

Source: S&P, FactSet, Fundstrat analysis.Rise of the Stock Pickers
Note: Shows the pairwise correlation in weekly stock returns for S&P 500 constituents, using a 13-week lookback window. Series is smoothed on a trailing 13-week basis. Period of analysis is from March 1999 through May 20, 2022. Transaction costs are not considered.

From Fig. 2, we see that the average pairwise correlation has been quite low recently. After peaking out at near 70% in the immediate aftermath of the pandemic, pairwise correlations rapidly fell, and have remained low since then. The low pairwise correlations mean that stocks are moving relatively independently from one another. In such an environment, the potential for stock pickers to exercise their skill is improved, as the number of independent investment opportunities is relatively large.

While the average pairwise correlation can give a general sense of how independently stocks are moving, it lacks some precision. Correlation, by definition, is bound to take values between -1 and +1, and when dealing with equity return correlations, those values typically range between 0 and +1. Also, correlation tends to lump together different drivers of return into a single number. Ideally, we’d like more granularity, so we can determine how much of a given stock’s return is attributable to drivers that we specify.

What it Means: Stocks in the energy sector tend to move with the price of oil, while financial companies can be impacted by movements in the yield curve. If we consider the correlations between energy stocks and financial stocks, the correlation may be low, which would indicate that these companies are being driven by stock-specific concerns. In reality, however, their returns may be driven primarily by independent “macro” drivers (i.e. the price of oil and the yield curve) instead of stock specific drivers like management or whether a firm beat analyst expectations.

Fortunately, factor models (a technique in the quantitative analysis toolkit) allow us to compute the degree to which a stock’s return is affected by specific macro drivers that we specify. We can specify different factors as part of a factor model based on what we think drives the returns of individual stocks. We can then “back out” the effects of those factors to arrive at a true value for the degree to which idiosyncratic concerns are driving individual stocks. So, returning to the example above, we can quantitatively determine what portion of a stock’s movement is from macro factors like the yield curve or oil prices versus what portion is driven by idiosyncratic considerations like quality of management and a company’s earnings performance.

We specify a simple factor model where we use two factors: the return of the overall market, and the return of the sector. Using this model, we compute the share of variation explained by the market and sector return for each individual stock. After backing out these effects, we are left with the idiosyncratic component of stock return, or the degree to which a stock’s return is NOT driven by macro (i.e. market and sector) concerns. When idiosyncratic return is high, stocks are being driven more by company-specific items, which should benefit stock pickers. Fig. 3 shows the history of the idiosyncratic return for S&P 500 constituents.

Fig. 3 – Idiosyncratic Return for S&P 500 Constituents

Source: S&P, FactSet, Fundstrat analysis.Rise of the Stock Pickers
Note: Shows the median idiosyncratic component of return for S&P 500 constituents. Idiosyncratic component of return is computed as 1 minus the share of variance explained by a factor model using the market and sector returns as factors. Factor model is estimated using rolling 13-week periods. Period of analysis is March 1999 through May 20, 2022. Transaction costs are not considered.

From Fig. 3, we see that the idiosyncratic return tends to drop to very low levels (below 25%) during periods of extreme market stress, such as the Financial Crisis and in the onset of the coronavirus pandemic. Lately, however, the idiosyncratic return has ranged between 45% and 60%, indicating that around half of the typical stock’s return can be explained by stock-specific factors. When idiosyncratic return is at these levels, competent stock pickers tend to benefit.

Recent Performance Around Earnings Beats/Misses

While the previous sections have demonstrated how to measure the breadth of investment opportunities for stock pickers, we now show that investors are distinguishing between outperformers and underperformers in recent months. We consider how investors are treating companies after quarterly earnings are released. Fig. 4 shows how investors are rewarding companies that beat earnings estimates (green bars) and punishing companies that miss their earnings targets (dark blue bars), going back over the past 10 earnings seasons.

Fig. 4 – Performance of Stocks Around Beats and Misses

Source: Bloomberg, S&P, Fundstrat analysis.Rise of the Stock Pickers
Note: Shows the 3-day relative return for stocks beating (green bars), in line with (gray bars) and missing (dark blue bars) earnings estimates. An earnings “beat” (“miss”) is defined as the stock reporting earnings at least 2% greater than (less than) consensus estimates. Period of analysis is from December 16, 2019 through May 23, 2022. Transaction costs are not considered.

From the chart, we see that during the most recent earnings season, companies that beat earnings have outperformed the index by an average of 1.2% in the 3-days following an announcement. On the other hand, companies that miss their earnings targets are being severely punished – during the Q1 2022 earnings season, these companies underperformed the S&P 500 by an average of 3.8% in the 3 days following an announcement.

This spread of 5.0% (between companies that beat and companies that miss) is the largest such spread over the past 10 earnings seasons. Investors are evaluating companies on their individual merits and rewarding or punishing them accordingly. To borrow a phrase from Brian Rauscher, our head of Portfolio Strategy and Asset Allocation, companies who miss earnings expectations or guide lower are “being taken to the proverbial woodshed.”

Conclusion

While the markets have struggled in 2022, the backdrop for stock picking has been favorable so far this year. The outperformance of value (relative to growth) has seen more stocks outperform the benchmark. Additionally, relatively low correlations and high degrees of idiosyncratic return mean stocks are moving more independently of each other, which allows stock pickers to realize success.

Investors have also focused on the merits of individual stocks, as companies that are beating earnings estimates are enjoying an historically large premium over companies that miss their earnings targets. Taken together, these metrics point to a continued favorable backdrop for stock picking. There is an old saying that during market panics “all correlations go to one.” This is the opposite of what is currently occurring in markets, despite their steep losses in 2022. The portion of stock performance being determined by idiosyncratic, as opposed to macro factors, is currently quite high by historical standards.


[1] See December Market Valuation

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