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Goldman Admits Reddit Raiders Could Crash the Entire Market

Goldman Warns If The Short Squeeze Continues, The Entire Market Could Collapse



This article was originally published by ZeroHedge.

Goldman Warns If The Short Squeeze Continues, The Entire Market Could Crash

Last Friday (Jan 22) we advised readers who thought they had missed the move in Gamestop (they hadn't), to position appropriately in the most shorted Russell 3000 names which included such tickers as FIZZ, DDS, BBBY, AMCX, GOGO and a handful of other names, as it was likely that the short-squeeze was only just starting.

We were right and all of the stocks listed above - and others - exploded higher the coming Monday, and all other days of the week, with results - encapsulated by the WallStreetTips vs Wall Street feud - that has become the top conversation piece across America, while on WSB the only topic is the phenomenal gains generated by going long said most shorted stocks. To wit, the basket of top shorts we compiled on Jan 22 has tripled in the past week.

And while some are quick to blame last week's fireworks on the "dopamine rush" of traders at r/wallstreetbets who seek an outlet to being "copped up with little else to do during the pandemic" (as Bloomberg has done, while also blaming widespread lockdowns and forgetting that it has been Bloomberg that was among the most vocal defenders of the very lockdowns that have given us the short squeeze of the century), the reality is that at the end of the day the strategy unleashed by the subreddit is merely an extension of the bubble dynamics that were made possible by the Federal Reserve (of which Bloomberg is also a very staunch fan) pumping trillions and trillions of shotgunned liquidity into a financial system where there are now bubble visible anywhere one looks. In short, main street finally learned that it too can profit from the lunacy of the money printers at the Marriner Eccles building, and some are very unhappy about that (yes, it will end in tears, but - newsflash - $300 trillion in debt and $120BN in liquidity injections monthly will also end in tears).

That aside, one week later, Goldman has finally caught up with what Zero Hedge readers knew one week ago, and all the way down to a chart showing a basket of the most-shorted Russell 3000 stocks...

... Goldman's David Kostin has published a post-mortem of what happened last week, writing that "the most heavily-shorted stocks have risen by 98% in the past three months, outstripping major short squeezes in 2000 and 2009."

He then points out something we discussed in "Hedge Funds Are Puking Longs To Cover Short-Squeeze Losses", noting that while aggregate short interest levels are remarkably low (imagine what would have happened has shorting been far more aggressive marketwide)...

... "the -4% weekly return of our Hedge Fund VIP list of the most popular hedge fund long positions (GSTHHVIP) showed how excess in one small part of the market can create contagion."

As an aside, and as we showed previously, as the most shorted stocks soared...

... hedge funds were forced to cover (as well as paying for margin calls), and as part of the broader degrossing they also had to sell some of the favorite hedge fund names across the industry, in this case represented by the Goldman Hedge Fund VIP basket.

Yet what may come as a surprise to some, even as hedge funds deleveraged aggressively and actively cut risk this week, gross and net exposures "remain close to the highest levels on record" (something which may come as a huge surprise to Marko Kolanovic who has been erroneously claiming the opposite), suggesting that if the squeeze continues, hedge funds are set for much more pain.

According to Goldman Sachs Prime Services, this week "represented the largest active hedge fund de-grossing since February 2009. Funds in their coverage sold long positions and covered shorts in every sector" and yet "despite this active deleveraging, hedge fund net and gross exposures on a mark-to-market basis both remain close to the highest levels on record, indicating ongoing risk of positioning-driven sell-offs."

With that in mind, here are Kostin's big picture thoughts:

It was a placid week in the US stock market – provided one was a long-only mutual fund manager. US equity mutual funds and ETFs had $2 billion of net inflows last week (+$10 billion YTD). Although the typical large-cap core mutual fund fell by 2% this week, it has generated a return of +1.3% YTD vs. S&P 500 down -1.1%. However, life was very different last week if one managed a hedge fund. The typical US equity long/short fund returned -7% this week and has returned -6% YTD.

With the average WSB portfolio up double digits this past week, one can see why hedge funds are upset. Anyway, moving on:

The past 25 years have witnessed a number of sharp short squeezes in the US equity market, but none as extreme as has occurred recently.In the last three months, a basket containing the 50 Russell 3000 stocks with market caps above $1 billion and the largest short interest as a share of float (GSCBMSAL) has rallied by 98%.  This exceeded the 77% return of highly-shorted stocks during 2Q 2020, a 56% rally in mid-2009, and two distinct 72% rallies during the Tech Bubble in 1999 and 2000. This week the basket’s trailing 5-, 10-, and 21-day returns registered as the largest on record.

Thanks Goldman, and yes, your "brisk assessment" would have been more useful to your clients if it had come before the event (like, for example, this) instead of after.

Kostin then goes on to point out that the "mooning" in the most shorted stocks took place even though aggregate short interest was near record low (imagine what would have happened had short interest been higher), which is odd because historically, "major short squeezes have typically taken place as aggregate short interest declined from elevated levels. In contrast, the recent short squeeze has been driven by concentrated short positions in smaller companies, many of which had lagged dramatically and were perceived by most investors to be in secular decline" to wit:

Unusually, the rally of the most heavily-shorted stocks has taken place against a backdrop of very low levels of aggregate short interest. At the start of this year, the median S&P 500 stock had short interest equating to just 1.5% of market cap, matching mid-2000 as the lowest share in at least the last 25 years. In the past, major short squeezes have typically taken place as aggregate short interest declined from elevated levels. In contrast, the recent short squeeze has been driven by concentrated short positions in smaller companies, many of which had lagged dramatically and were perceived by most investors to be in secular decline.

Of course, there is nothing "historical" about what happened last week, because - as we all know - the biggest difference between the typical short squeeze of the past and the recent rally in heavily-shorted stocks "was the degree of involvement of retail traders, who also appear to have catalyzed sharp moves in other parts of the market." Why thank you WSB, but that's ok - you will be handsomely rewarded.

Last week we discussed the surging trading activity and share prices of penny stocks, firms with negative earnings, and extremely high-growth, high-multiple stocks. These trends have all accompanied a large increase in online broker trading activity. A basket of retail favorites (ticker: GSXURFAV) has returned +17% YTD and +179% since the March 2020 low, outperforming both the S&P 500 (+72%) and our Hedge Fund VIP list of the most popular hedge fund long positions (GSTHHVIP, +106%).

So why does this matter? One simple reason: contrary to the bizarrely nonchalant optimism spouted earlier this week by JPMorgan's Marko Kolanovic who said "any market pullback, such as one driven by repositioning by a segment of the long-short community (and related to stocks of insignificant size), is a buying opportunity, in our view," Goldman has a far more dismal take on recent events, and writes that "this week demonstrated that unsustainable excess in one small part of the market has the potential to tip a row of dominoes and create broader turmoil."

He then picks up on what he said last weekend when responding to Goldman client concerns about a stock bubble, which we summarized in "Goldman's Clients Are Freaking Out About A Stock Bubble: Here Is The Bank's Response", and which turned out to be 100% warranted, and writes that "most of the bubble-like dynamics we highlighted last week have taken place in stocks constituting very small portions of total US equity market cap. Indeed, many of the shorts dominating headlines this week were (prior to this week) small-cap stocks. But large short squeezes led investors short these stocks to cover their positions and also reduce long positions, leading other holders of common positions to cut exposures in turn."

As a result, Goldman's Hedge Fund VIP list declined by 4%. Which is a problem because as Kostin concludes, "in recent years elevated crowding, low turnover, and high concentration have been consistent patterns, boosting the risk that one fund’s unwind could snowball through the market."

Translation: if WSB continues to push the most shorted stocks higher, the entire market could crash.

And since Kostin admits that "the retail trading boom can continue" as "an abundance of US household cash should continue to fuel the trading boom" with more than 50% of the $5 trillion in money market mutual funds owned by households and is $1 trillion greater than before the pandemic, what happens in the coming week - i.e., if the short squeeze persists - could have profound implications for the future of capital markets.

Tyler Durden Sat, 01/30/2021 - 18:30

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U.S. COVID-19 cases increased threefold over past month – Walensky

The United States has seen a threefold increase in daily COVID-19 cases over the past month and a steady increase over the past five weeks, U.S. Centers…



U.S. COVID-19 cases increased threefold over past month – Walensky

WASHINGTON, May 18 (Reuters) – The United States has seen a threefold increase in daily COVID-19 cases over the past month and a steady increase over the past five weeks, U.S. Centers for Disease Control and Prevention Director Rochelle Walenksy said on Wednesday.

The seven-day average of daily cases was up 26% from the previous week to 94,000 cases per day, Walensky said at a White House briefing. The seven-day average for hospitalizations was up 19% to about 3,000 per day and the average for deaths was 275 per day, she said.

People queue to be tested for COVID-19 in Times Square, as the Omicron coronavirus variant continues to spread in Manhattan, New York City, U.S., December 20, 2021. REUTERS/Andrew Kelly/File Photo

“We of course must remember that each person lost to COVID-19 is a tragedy and that nearly 300 deaths a day is still far too many,” said Walensky.

Reporting by Susan Heavey and Ahmed Aboulenein

Our Standards: The Thomson Reuters Trust Principles.


Reuters source:


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In stop-COVID19 trial, Brensocatib did not improve condition of patients with severe COVID-19

Session:  D17, Top Knowledge in COVID Date and Time:  9:25 a.m. PT, Wednesday, May 18,2022 Location:  Room 2018/2020 (West Building, Level 2), Moscone…



Session:  D17, Top Knowledge in COVID
Date and Time:  9:25 a.m. PT, Wednesday, May 18,2022
Location:  Room 2018/2020 (West Building, Level 2), Moscone Center

Credit: ATS

Session:  D17, Top Knowledge in COVID
Date and Time:  9:25 a.m. PT, Wednesday, May 18,2022
Location:  Room 2018/2020 (West Building, Level 2), Moscone Center


ATS 2022, San Francisco, CA – Brensocatib did not improve the clinical status of patients hospitalized with severe SARS-CoV-2 infection in the double-blind randomized, placebo-controlled STOP-COVID19 multicenter clinical trial, according to research published at the ATS 2022 international conference.

The study, which began in June of 2020, took place at 14 UK hospitals, where participants were randomized to receive 25 mg daily of brensocatib or placebo for 28 days.  One-hundred ninety patients received brensocatib, while 214 received placebo.

All patients in the study had confirmed SARS-CoV-2 infection and at least one risk factor for severe COVID-19, such as requiring supplemental oxygen, Individuals on mechanical ventilation were excluded from the study.  All participants received standard of care treatment.

“Treatments currently available to treat COVID-19, such as dexamethasone and anti-IL-6 antibodies, reduce inflammation, but their effect is not primarily on neutrophils or neutrophilic inflammation,” said presenting author Holly Keir, PhD, postdoctoral researcher, University of Dundee School of Medicine, Dundee, United Kingdom. “We performed the STOP-COVID trial to test the hypothesis that directly targeting neutrophilic inflammation by inhibiting dipeptidyl peptidase-1 (DPP1) would provide additional benefits to patients with severe COVID-19 on top of standard of care.”

Severe COVID-19 infection is primarily caused by an excessive and damaging immune response to the virus.  A number of different immune cells are involved in this response, including neutrophils. Neutrophils release enzymes and other substances that cause severe lung damage. Studies have consistently shown that high levels of neutrophilic inflammation are associated with worse outcomes in COVID-19. 

Brensocatib is an investigational oral inhibitor of DPP1, an enzyme responsible for the activation of neutrophil serine proteases.

In STOP-COVID19, time to clinical improvement and time to discharge were not different between groups. Mortality was 10.7 percent and 15.3 percent in the placebo and brensocatib treated groups, respectively. Oxygen and new ventilation use were also numerically greater in the brensocatib treated patients. Prespecified subgroup analyses based on age, sex, baseline severity, co-medications and duration of symptoms supported the primary results. Adverse events were reported in 46.3 percent of placebo treated patients and 44.8 percent of brensocatib treated patients.

The researchers also conducted a sub-study at two study sites to directly measure inflammation in patients receiving DPP1 inhibition or placebo.  They observed a strong anti-inflammatory effect of DPP1 inhibition on neutrophil protease enzymes.  Active blood neutrophil elastase levels were reduced by day eight in the treatment group and remained significantly lower up to day 29.

“Although we did not find a beneficial effect of treatment in this population, these results are important for future efforts to target neutrophilic inflammation in the lungs. STOP-COVID19 is the largest completed trial of DPP1 inhibition in humans and we have performed extensive characterization of how DPP1 inhibition affects the immune system’s response,” noted Dr. Keir. “Using state-of-the-art proteomics (the study of the structures, functions, and interactions of proteins) we have already seen important changes in neutrophils with DPP1 inhibition that will help us to better understand the potential role of this treatment in other diseases.”

One of these diseases is bronchiectasis, where a phase 2 trial published in 2020 showed that brensocatib reduced the risk of exacerbations.

The STOP-COVID19 study was an investigator-initiated study sponsored by the University of Dundee and funded by Insmed Incorporated.



You may be interested in other newsworthy research on pulmonary infections, such as “A Nine-Gene Blood-Based Signature Meets the World Health Organization Target Product Profiles for Diagnosis of Active Tuberculosis and Predicting Progression from Latent to Active Disease.”


Grant Hill

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Next-generation weather models cross the divide to real-world impact

Each winter, spring, and summer, extreme weather forecasters and researchers meet to test the latest, most promising severe weather forecast tools and…



Each winter, spring, and summer, extreme weather forecasters and researchers meet to test the latest, most promising severe weather forecast tools and innovations to see how they perform in real-world settings.

Credit: NOAA

Each winter, spring, and summer, extreme weather forecasters and researchers meet to test the latest, most promising severe weather forecast tools and innovations to see how they perform in real-world settings.

These testbed experiments, orchestrated by the National Oceanic and Atmospheric Administration (NOAA), forecast winter storms, severe thunderstorms, and flash flooding, respectively.

The Hydrometeorology Testbed recently held the 12th annual Winter Weather Experiment (WWE). An immersive, collaborative, “research-to-operations” experience, it brought together members of the forecasting, research, and academic communities to evaluate and discuss winter weather forecast challenges.

“We generate operational forecasts using new inputs or new models testing how well they work,” said Keith Brewster, senior research scientist and operations director of the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. “If we promise a forecast where thunderstorms occur, can we expect a forecaster to use it?”

Humming along in the background of the experiment are supercomputers at the Texas Advanced Computing Center (TACC) — among the fastest available to academic researchers in the world.

The CAPS team began using TACC systems for the Hazardous Weather Testbed Spring Experiment in 2011 to better predict severe thunderstorms. They computed at that time on the original Stampede supercomputer at TACC — the 6th fastest in the world in its prime. From 2017-2021, they used Stamepde2 (12th fastest) through the Extreme Science and Engineering Discovery Environment (XSEDE). Since 2021, they have used Frontera, the fastest university supercomputer in the world and currently the 13th fastest overall.

“What TACC and XSEDE offer us is the ability to do these real-time, or near-real-time experiments,” Brewster said.

The CAPS team submits their forecasting simulations by 10:00pm, after weather observations and other input data comes in for the 00 UTC cycle. The simulations run overnight and are ready by 8:00am the next morning, predicting weather events out to three-and-a-half days.

“On Stampede, we worked with TACC to have a special queue set up where we have a dedicated number of cores allocated to us,” Brewster said. This type of ‘urgent computing’ has become a hallmark of TACC, enabling the center to forecast hurricane storm surge, monitor space junk in low Earth orbit, and power COVID-19 models. “More recently, on Frontera, the capacity is such that we can run in the regular queue, using a VIP priority, making our use more efficient and less disruptive to other research users.”

This year’s Winter Weather Experiment had three key science goals: to subjectively gauge the utility of convection-allowing model (CAM) forecasts to improve two-to-three day snowfall forecasts; objectively score the snowfall forecasts using community standard verification systems; and determine the optimal combination of physics to use in next-generation models.

The team was primarily interested in predicting the amount of snowfall accumulation, but they also tested their ability to determine the differences between snow, sleet, and freezing rain in forecasts, and predict other facets of winter weather, like wind speed.

“Giving forecasters the opportunity to use these experimental models in real situations allows forecasters and researchers to determine the strengths, operational challenges, and forecaster usability early on in the development stage,” said James Correia Jr., coordinator for the Hydrometeorology Testbed. “This allows us, together, in NOAA testbeds, to make improvements in our forecasting process, models, and the way we approach and solve research and operational challenges.”

Recent testbed programs have also included the important task of evaluating NOAA’s next-generation weather model, the FV3 model. This model has shown success in global-scale forecasting, and the agency plans to also utilize it operationally for much higher resolution regional modeling as represented in the high-impact testbeds. The new multi-scale forecast system is known as the Unified Forecasting System (UFS).

“In addition to real-time testing, CAPS has been using TACC supercomputers to re-run cases to identify the root-cause of issues that were identified during prior testbeds,” Brewster said. “This leads to tuning and other enhancements to the original codes.”

The Winter Weather Experiment ran for 27 case days on Frontera in near-real-time over the course of the winter, including objective verification and machine learning training — a forward-looking aspect of the research. Brewster presented the results as a webinar organized by NOAA in March 2022.

Following the experiment, researchers typically do more detailed studies on specific facets of the forecasts, with funding from NOAA’s Weather Program Office as part of the Testbed competition in collaboration with NOAA’s Weather Prediction Center and Storm Prediction Center – both divisions of the National Weather Service.

Testing Ensemble Consensus Methods

Most weather watchers are familiar with the idea of ensemble models — the swarms of tracks that represent the results from various simulations, which are averaged and interpreted by weather forecasters.

Using Frontera, Brewster’s team generates real-time ensemble forecasts.

“In decision theory, it has been shown that when you get a consensus of experts, you get better advice than from a single person,” Brewster said. “Thanks to TACC, we can generate 13 models — 13 ‘experts’ predicting what the weather is going to be. From there, we’re working on how to develop ensemble consensus products that best help improve forecasts.”

Sometimes useability by a human operator trumps pure prediction skills. Communicating the consensus decision from an ensemble of forecasts is such an example.

“We researchers are in there, observing and participating, for one week — as if we were in the weather office, creating forecasts, so people like myself can see the issues,” Brewster explained. “We try to be realistic: can someone really look at ten to 15 models? Or does it create more uncertainty?”

One approach the CAPS team has been exploring for ensemble consensus methods is the local probability match mean (LPM) method. The LPM method divides an area into patches, calculates the atmospheric dynamics over that patch, and distributes the results locally. (Nathan Snook and the CAPS team described the method, and compared various ways of computing this mean, in a 2020 paper in Geophysical Research Letters.)

An assessment of the accuracy by NOAA showed the local probability match mean (LPM) performed slightly worse than probability match (PM) mean in objective precipitation scoring.

“But this is where the testbed activities come in,” Brewster said. “When a human looks at a forecast, they’re not looking at raw numbers at a site. They’re looking at the shape — consensus reflectivity — and in this respect, LPM was deemed to be better. That was a win for our team.”

The LPM has since been implemented in NOAA’s operational High Resolution Ensemble Forecast system. This is the goal of the NOAA Testbed program: taking research ideas and getting them through testing and evaluation in quasi-operational settings to actual operational deployment.

“That’s what we call technology transfer,” Brewster said. “There’s a tech divide where researchers like our team work on models, produce papers, and it can be hard to get new models or concepts into the operations. Tech transfer happened because it was proven, not just running on TACC and to other researchers, but to other forecasters. That gets us over the divide from journal articles to impacting real-world forecasts.”

The 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment is being held from May 3 – June 2 and is again leveraging supercomputers at TACC. For more information about the upcoming experiment, contact: Keli Pirtle, Public Affairs Specialist, NOAA Communications,

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