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Identifying Crowded Names - Q3 2021 Earnings Season

Crowding continues to be a popular topic of conversation among investors. Crowded stocks can exhibit exacerbated pricing pressure when material information reaches the market. In addition, earnings releases can increase price volatility, making earnings a strong catalyst for crowded names.

The prevalence of short squeeze events in 2021 has made it even more critical for managers to stay on top of the most popular longs and shorts in the market to avoid unanticipated losses. As we have done in our prior edition, we will be leveraging data from our Data Cloud partners, Wolfe Research and S3 Partners, to demonstrate approaches to identify particularly risky names heading into the Q3 earnings season.

Screening for Crowded Names

This quarter, we will once again leverage the Wolfe Research QES US Broad (“Wolfe US”) risk model and the S3 Partners’ Short Interest and Securities Finance dataset to help define crowded names especially susceptible to unexpected price movement during earnings.

Our analysis will use the Hedge Fund Crowding factor from the Wolfe US risk model, which measures the sensitivity of a stock to a long/short basket constructed using hedge fund intensity (% of float) and level (market value) based on 13F filings. We identify crowded names as those with high exposure to the HF Crowding factor on the long side of the market.

S3’s Squeeze Risk Score identifies crowded stocks on the short side of the market that are particularly at-risk for a squeeze event. This metric highlights the risk (scale:0-100) that crowded shorts could be forced to reduce or liquidate a short position - based on the following:

  • the total short dollar amount in the stock
  • liquidity in the stock loan market
  • trading capacity
  • short interest as a true percentage of float
  • increased stock lending costs
  • mark-to-market losses

The Squeeze Risk Score provides us with a normalized view of the conditions necessary to produce a squeeze to effectively evaluate crowded names across sectors, regions, and market cap. The Squeeze Risk measure alone doesn’t indicate whether or not a squeeze will or won’t occur. Instead, the score quantifies a stock’s susceptibility to being squeezed. For example, suppose a stock with a high Squeeze Risk Score sees a positive price return from a market catalyst such as an earnings surprise or industry/company-specific news. In that case, it is likely to feel a squeeze as short-sellers rush to cover their positions. S3 notes that stocks with a score of 50 or above are considered to be crowded and susceptible. Below, we show a more detailed breakdown of the interpretation.


Another element essential to consider when evaluating the likelihood that earnings will be the spark that ignites a squeeze is analyst sentiment. The Wolfe US Revisions factor measures the sensitivity of a stock to a long/short basket constructed using a combination of 3-month changes in mean consensus EPS and Sales on a Next-Twelve-Months basis. A stock with a positive Revisions exposure has increased analyst outlook regarding its likelihood of beating prior earnings estimates. Conversely, a stock with a negative Revisions exposure has a downgraded probability of beating its previous earnings estimates. As a result, long, crowded names with high Revisions exposure could see a significant sell-off following a negative earnings surprise, and short, crowded names with low Revisions exposure could rally in the case of a positive earnings surprise, setting off a dramatic squeeze.

With these criteria in mind, we took the Russell 3000 index, screened for an average daily trading volume greater than $5 million and market cap greater than $500 million, and built lists for long crowded names and short, crowded names. The Long Crowded list contains stocks with Wolfe HF Crowding and Revisions exposures > 1. The Short Crowded list includes stocks with Wolfe Revisions exposure < -1 and an S3 Squeeze Risk Score > 50.


Resulting Crowded Stocks

In total, we identified 130 stocks in the Long Crowded Names group and 134 in the Short Crowded Names group. It’s worth noting that when we ran the same analysis using the same criteria in Q2, the number of stocks in the Long Crowded Names group was similar (Q2: 128). In contrast, the number of stocks in the Short Crowded Names group was significantly lower this quarter (Q2: 82). Energy, Financials, and Health Care stocks are driving the marked increase in the number of short crowding names compared to last quarter.


The tables below display the top 3 crowded names in each sector of the long and short crowding groups.

Long Crowded Group Top 3 Names by Sector


Short Crowded Group Top 3 Names by Sector


How to Manage Crowding

If you find crowded names in either the long or short book of your portfolio, there are actions to take that can ensure you are well-positioned heading into earnings season.

The first step is to understand what particular names are the sources of crowding risk. Next, what is the conviction level of these names? If you are long or short a crowded stock that you strongly believe in, you may determine it best to ride it out. If you feel there is a strong possibility that the long stock in question beats earnings or short stock misses, you may not need to adjust at all. On the other hand, if there is uncertainty around the potential outcome and the conviction doesn’t outweigh the crowding risk, it could be worthwhile to consider a trim or even a closing of the position.

The other option is to balance the potential systematic forces at play through hedging. For example, if you find that the long sleeve or your portfolio contains a high degree of long crowding risk, targeting single stocks or a basket on the short side that would likely experience headwinds from such an event could mitigate losses on the long side.

US & Global Market Summary

US Market: 10/11/21 - 10/15/21

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US Stock Market Cumulative Return: 10/11/2021 - 10/15/2021
  • U.S. stocks notched their biggest weekly gains in months following a strong streak of earnings reports. The S&P 500 was up 1.8% for the week, its best weekly performance since July, and the Dow gained 1.6%, its biggest weekly gain since June. The Nasdaq Composite climbed 2.2% for the week.
  • All but one of the 19 companies in the S&P 500 that reported quarterly results topped analysts’ profit forecasts.
  • Goldman Sachs rose 3.8% and Alcoa surged 15.2% after it beat earnings expectations and announced a dividend payment and buyback of its stock.
  • Retail Sales unexpectedly rose in September. Sales at stores, restaurants and other retail establishments rose 0.7% from August instead of falling, as economists forecast.
  • Data released this week showed a gauge of consumer prices rose more than expected in September.
  • Energy markets extended a run-up hat has pushed oil and gas prices to multiyear highs and strained already snarled supply chains.

Normalized Factor Returns: Axioma US Equity Risk Model (AXUS4-MH)

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Methodology for normalized factor returns
  • Earnings Yield takes the top spot for the 2nd straight week following its recent extended slide.
  • Growth snapped back after recent weakness reversing its trajectory further away from extremely oversold status.
  • Size continues to rebound.
  • Market Sensitivity (Beta) climbed for the 7th consecutive week as it inches closer to overbought territory.
  • Value reversed course to exit its brief stay in overbought terrain .
  • Medium-Term Momentum fell for the 5th straight week.

Normalized Factor Returns: Axioma Worldwide Equity Risk Model (AXWW4-MH)

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Methodology for normalized factor returns
  • As with the US factor leaderboard, global Earnings Yield takes this week’s top spot, but sitting deeper in oversold territory, it may have more room to rise in the coming weeks.
  • Exchange Rate Sensitivity moves up and deeper into overbought terrain.
  • Size showed strength after recent weakness and exits its brief liaison with oversold status.
  • Value abruptly reversed course following last week’s top ranking to finish at the bottom of this week’s global factor leaderboard.


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