Before we dive into this week's topic on crowding, I wanted to let you know that a recording of our joint webinar with Qontigo: Macro Matters, Even for Fundamental Managers is now available to view on our website in case you missed the live webinar. We hope that you enjoy it.
Crowding is 2021’s hot topic, especially as investors dive into the deep end of the Q2 2021 earnings season. Understanding the crowdedness of portfolio names is critical during earnings season to avoid pain if the market unexpectedly puts price pressure on heavily positioned stocks. Moreover, with short squeezes and unwind events becoming commonplace in the current market environment, it’s more important than ever to keep a strong pulse on the market’s favorite longs and shorts.
This week, we’ll be leveraging data from our Data Cloud partners, Wolfe Research and S3 Partners, to illustrate methods to screen portfolios for risky earnings season names and highlight some of the market’s most crowded stocks.
Innovative Data Helps Screen for Crowded Names
We’ll be looking to 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 that are 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. Names with high exposure to the HF Crowding factor are identified as crowded on the long side of the market.
S3’s Squeeze Risk Score identifies stocks that are crowded on the short side of the market and are particularly at-risk for a squeeze event. This metric highlights the risk (on a 0-100 scale) that crowded shorts could be forced to reduce or liquidate a short position - based on the following:
- 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.
It is important to note that the Squeeze Risk Score alone does not definitively point to a squeeze event occurring; rather, it points to stocks with the necessary conditions for the squeeze to occur. In other words, if these stocks experience upward price pressure from a market catalyst (i.e., retail traders or earnings surprise), the conditions are ripe for a squeeze. S3 guides that names with a score of 50 or higher are crowded and exhibiting squeeze warning signs. We can interpret the scores even further, as we note below.
We’ll also borrow methodology from past earnings season analysis, by using revisions to call attention to names vulnerable to unexpected price pressure due to earnings surprise. 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 over the next 12-months. Stocks with high positive Revisions exposure are expected to beat earnings but may experience significant downward price pressure if they miss, making them dangerous longs. Conversely, we expect stocks with high negative exposure to miss on earnings and may have heavy upward pressure if they beat, making them contenders for a squeeze if the conditions are right.
Results for the Highly Crowded Names
Putting all of the above together, we screened for names in the Russell 3000 with average daily trading volume greater than $5M and market capitalization greater than $500M that exhibited the characteristics noted in the above table. The long crowding group comprises the names with HF Crowding & Revisions exposures >1, while the short crowding group comprises names with Revisions exposures <-1 and Squeeze Risk Score over 50.
These screens resulted in 128 names in the long crowding group and 82 names in the short crowding group.
The tables below display the top 3 crowded names in each sector of the long and short crowding groups. (Note: we’ve removed any names that have already reported earnings)
Long Crowded Group Top 3 Names by Sector
Short Crowded Group Top 3 Names by Sector
We’ve Identified Crowded Names, Now What?
After highlighting the crowded names in your portfolio, what are the best actions to take? Much of this comes down to your fundamental conviction in the stock along with the construction of the rest of your portfolio.
If your level of fundamental conviction in a name does not justify the level of crowding risk, you may consider trimming or even closing the position. However, for long or short crowded names with high fundamental conviction, you may consider riding out the crowding risk. If you choose the latter route, constructing positions on the opposite side of the book that can hedge the crowding risk could help mitigate some of the portfolio swings during crowding and subsequent unwinding events.
For example, suppose the short book of your portfolio shows high exposure to short interest, and a squeeze event feels imminent. In that case, you can add to positions in the long book that show signs of probable upward price pressure to offset potential near-term short losses.
Regardless of the downstream action, the combination of unique data and screening techniques allows us to react to earnings season-induced crowding risk quickly.
If you are interested in understanding how the above datasets and models from S3 Partners and Wolfe Research can help you uncover at-risk crowding forces within your own portfolio, please click here to request a complimentary analysis of your portfolio.
US & Global Market Summary
US Market: 07/26/21 - 07/30/21
- The US market ended the week slightly down after modest weakness on light volume Friday, but the major indices all enjoyed a strong July despite increased volatility around the Delta variant. The S&P 500 was up +2.3% in July for a sixth straight positive month.
- Fed Chairman Powell noted on Wednesday that the Fed will continue to take a measured approach to any changes to current policy, assuaging investors waiting for work out of this week’s meetings.
- After the busiest earnings week in US history, the prevailing narrative appears to be better-than-expected 2Q revenue and profitability, alongside caveats of harder comps and higher investment coming in 2H. Some of the tech behemoths sold off after reporting, including AMZN, FB, and AAPL.
- The market also digested the impact of China’s harsh regulatory changes as many Chinese tech stocks took a hit and managers grappled with the fact that there is no floor of risk when investing in Chinese securities.
Normalized Factor Returns: Axioma US Equity Risk Model (AXUS4-MH)
Methodology for normalized factor returns
- The meteoric rise in Earnings Yield continued as it was again the week’s top factor. It now sits at +0.76 SD above the mean merely a week after crossing into positive normalized space. This factor had bottomed out at -1.86 SD below the mean on 6/24.
- Profitability also enjoyed more positive momentum as it entered Overbought space at +1.06 SD above the mean.
- Value continued to recover from a recent low of -2.22 SD below the mean on 7/8, although it remains an Oversold factor.
- Growth appears to have peaked at +2.12 SD above the mean on 7/23 and has since ticked down enough to lose its Extremely Overbought label (now at +1.95 SD above the mean).
- Market Sensitivity crossed into negative normalized territory after tumbling out of Overbought space two weeks ago. This factor was at +1.7 SD above the mean on 7/1.
- Volatility was again the worst performing factor as it fell another 0.47 standard deviations into negative normalized territory at -0.08 SD below the mean. If you missed our recent deep dive into this factor, here’s last week’s Factor Spotlight.
- US Total Risk (using the Russell 3000 as proxy) decreased by 34 basis points.
Normalized Factor Returns: Axioma Worldwide Equity Risk Model (AXWW4-MH)
Methodology for normalized factor returns
- Profitability continued to bounce back towards the mean from a recent bottom of -2.42 SD below the mean on 6/28.
- Value rallied enough to lose its Extremely Oversold label, as it now sits at -1.8 SD below the mean after a recent bottom of -2.22 SD below the mean on 7/21.
- Global Earnings Yield climbed higher into Overbought space, now sitting at +1.53 SD above the mean.
- Growth peaked at 1.56 SD above the mean on 7/26 and has since been ticking down, remaining an Overbought factor at +1.48 SD above the mean.
- Market Sensitivity continued its decline and is at the threshold of earnings an Oversold label at -0.9 SD below the mean.
- Exchange Rate Sensitivity is also on the cusp of becoming an Oversold factor at -0.95 SD below the mean.
- Global Volatility saw another major drawdown in normalized return after crossing into negative normalized space last week. It now sits at -0.79 SD below the mean.
- Global Total Risk (using the ACWI as proxy) decreased by 11 basis points.