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Identifying Crowded Names - Q4 2021 Earnings Season

There is no shortage of systematic distortion in the market as we kick off Q4 2021 earnings season. As we have in the past, we will highlight the most crowded stocks from both a long and short perspective as earnings calls begin. 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.

In addition to the familiar analysis we’ve presented in the past, we will also look to Quant Insight’s macroeconomic factors to understand the additional macro pressures these crowded stocks may continue to face. As noted last week, we believe the Quant Insight dataset will be essential for investors to stay informed about potential macro headwinds that their portfolios may face, especially during 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 that are particularly at-risk for a squeeze event. This metric (scale:0-100) highlights the risk 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 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 will not 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 the short-sellers rush to cover their positions. S3 notes that stocks with a score of 50 or above are considered crowded and especially susceptible to a squeeze event. 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 187 stocks in the Long Crowded Names group and 63 in the Short Crowded Names group. These results differ pretty significantly from what we saw in Q3. Under the same criteria three months ago, the number of stocks in the Long Crowded Names group was much lower (130), and the number of stocks in the Short Crowded Names group was much higher (134). Consumer Discretionary, Health Care, Industrials, and Information Technology stocks are driving the marked increase in the number of long crowded names and decrease in short crowded 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


The Fed’s Impact

Last week, we utilized Quant Insight’s macroeconomic model and, more specifically, three key factors to observe the sensitivities of stocks to a newly hawkish posture from the Fed. This week, we’ve isolated long crowded stocks with downside sensitivity and short crowded stocks with upside sensitivity to such a macro environment. Below are the top five names in each category based on the implied price impact of a one standard deviation move in each Fed-related factor. These long crowded stocks could experience increased sell-offs due to their preference for easier financial conditions (i.e., low-rate environment), and the short crowded stocks could see the opposite as they respond well to rising rates. In addition, the R-Squared value, which measures the portion of asset volatility the macro model explains in aggregate, is also significant for these names, which indicates that these are “macro-driven” stocks.


How to Manage Crowding

Suppose you find crowded names in either the long or short book of your portfolio. In that case, there are actions to take that can ensure you are well-positioned heading into earnings season and the broader macro-driven environment.

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: 01/10/22 - 01/14/22

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US Stock Market Cumulative Return: 1/10/2022 - 1/14/2022
  • The Nasdaq fell 0.28%, marking its third negative week in a row, while the Dow and S&P 500 lost 0.88% and 0.30%, respectively.
  • Bank stocks, which had outperformed in recent weeks as interest rates moved higher, were broadly lower as their reports underwhelmed investors despite strong headline numbers.
  • Retail sales sank 1.9 percent in December after Americans cut their spending in the face of product shortages, rising prices and the onset of the omicron variant.
  • The Labor Department reported that consumer inflation jumped at the fastest pace in nearly 40 years last month, a 7 percent spike from a year earlier.
  • January’s preliminary consumer sentiment reading from the University of Michigan came in lower than expected as Americans reported higher long-term inflation expectations.

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

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Methodology for normalized factor returns
  • Value added afterburners to its recent rocket trajectory to once again secure the week’s top spot as it approaches extremely overbought status.
  • Earnings Yield maintains its steady multi-month climb up the charts and sits at the cusp of extremely overbought terrain at +1.88 SD above the mean.
  • Market Sensitivity continues its show of strength following a string of bottom finishes last month.
  • Volatility moves higher for the third consecutive week following a ten-week slide.
  • Profitability eked out some gains following an extended slump.
  • Medium-Term Momentum fell once more to move deeper into oversold terrain.
  • Growth plunged for the second straight week to finish at the bottom of the U.S. leaderboard.
  • U.S. Total Risk rose by 0.01% this week.

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

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Methodology for normalized factor returns
  • Global Value - in parallel with its U.S. counterpart - zooms to the top spot for a second straight week and sits at the doorstep of overbought terrain.
  • Market Sensitivity continues to show strength as it crossed into positive territory after languishing in oversold status only two weeks prior.
  • Exchange Rate Sensitivity shows little indication of slowing its climb as it moves upwards for the third straight week.
  • Size continues to show weakness but avoids replicating its bottom finishes from the two previous weeks.
  • Growth slid lower to continue on the path of its recent reversal.
  • Profitability crashed for the fifth consecutive week as it crossed into oversold territory.
  • Global Total Risk fell by 0.11% this week.


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