Omega Point Blog

Turning Analysis into Action: Hedge Baskets for Momentum Flow

Kevin Wahlberg

Turning Analysis into Action: Hedge Baskets for Momentum Flow

March 28, 2021

In our prior two Factor Spotlights, Who’s Going With the (Momentum) Flow? and Is Momentum Flow Riding the Interest Rate Rollercoaster?, we have explored and analyzed the themes of momentum rotation and interest rates. Based on the many inquiries and portfolio evaluation requests we’ve received since we first published on these topics, we’re encouraged that so many in the investment community are taking proactive steps to manage their exposures to these unprecedented, pandemic-related momentum flows. As a reminder, it’s still not too late to reach out to us for a free portfolio evaluation.

By way of recap, an estimated $2 trillion in quantitative strategies that are heavily reliant on momentum factors are in the process of rebalancing. Because the popular 12M-1M trailing period used for medium-term momentum calculations is sliding past last year’s pandemic-induced downturn, the stocks that have led momentum factors will soon be replaced by a new set of leaders. A custom factor emerged from our analysis, known as “Momentum Flow”, which quantifies the likely rotation in medium-term momentum when the upcoming March month-end exposures are calculated.

Given the current rising rate environment which has been causing pain for the portfolio managers, we also paired the Momentum Flow analysis with the Interest Rate Beta factor from the Wolfe Research QES US Broad risk model (“Wolfe US Broad”) to isolate the stocks that have strong positive or negative sensitivity to rising rates.

How can we use this analysis to our advantage? There are several ways that the investment community can put this into practice. This week, we will highlight two methods which will prove effective to brace against the potential momentum shift.

  1. Identifying exposures to these key factors and areas of interest that exist within an existing portfolio or coverage list.
  2. Creating hedges to reduce any unwelcomed negative exposure to Momentum Flow and Interest Rate Beta.

Security Search

For this example, we will show how a US-centric Healthcare long/short PM can identify short candidates securities in their sector that are negatively exposed to momentum flow.

Starting with a US investable universe, we can first filter down to a sample coverage list of large cap, liquid, Health Care stocks. From there, we can add the relevant characteristics, factor exposures, and content sets we would like to use to compare and evaluate our universe. Recall that we were able to load the custom Momentum Flow factor exposure as a content set into Omega point and we can sort by that exposure here to easily identify the stocks that are likely to experience headwinds or tailwinds as a result of the current momentum rotation.

The stocks that fall at the lowest end of Momentum Flow in this screen also have substantially negative exposure to the Interest Rate Beta factor. These stocks are not only the most likely to see outflows during the current momentum rebalance but will also feel downward price pressure as interest rates rise.


We could filter the screen further for individual long or short ideas, apply this same filtering logic to a live portfolio, or, we could implement the exercise into a hedge.

Short Hedge Basket Creation

ETFs are often a popular method of hedging as they are a quick and liquid tool that can control exposure to a particular market, sector, or broad-based factors. However, the downside is that they don’t offer the flexibility to truly cater a hedge to a particular portfolio or to unique or proprietary factors. As an alternative, we can build a custom basket of stocks where we have full control to better meet our objectives.

To illustrate this, we will start with a commonly utilized ETF, IWV (iShares Russell 3000 ETF), as our base hedge. From there, we will apply our knowledge surrounding the momentum rotation and interest rates, incorporate a custom content set (Momentum Flow) and a risk model factor (Wolfe’s Interest Rate Beta) to build, what should be, a more targeted hedge.

IWV - Momentum Flow
Honing in on IWV with respect to our exposures in question, we first see that the ETF has a negative Momentum Flow exposure at -0.18, meaning it has more exposure to stocks likely to experience outflows during the momentum rotation. Because we are looking to short the ETF, this is a positive for us.



IWV - Interest Rate Beta
IWV is also underexposed to Interest Rate Beta meaning it will likely feel systematic downward pressure as interest rates rise. This also directionally works in our favor.


Although IWV seems to help from a starting point, there are a lot of other systematic risks that we are taking on when shorting this ETF. Can we enhance our desired exposures further while creating an investable basket with a lower degree of overall total risk?

To answer that question, we built a hedge basket using a subset of the IWV universe to target significant underexposure to Momentum Flow and Interest Rate Beta. By filtering out stocks positively exposed to these factors, we are left with a more focused universe from which to build from.

Exposure Comparison
Below, while we do see moderate drifts in other factor exposures, by far the largest delta comes from Interest Rate Beta which is exactly what we would hope to see. Momentum moving does not bother us much in and of itself. What we want to know is how much more exposed will we be to Momentum through the factor rotation.

The Momentum Flow we see in our enhanced hedge basket is far lower than the ETF, which means the short basket will take much greater advantage of the outflows from the former momentum leaders as they sell off.

Total Risk
At the overall level, the basket has a lower degree of total risk (predicted annualized standard deviation) than IWV as well. So, not only are we taking on less risk, but we are taking on smarter risk. The risk of the basket is far more heavily influenced by factors that we have confidence in in the near term. In that same vein, we are less susceptible to unintended or unwanted systematic risks.


Basket Simulation
Because the momentum rotation and rising rates are themes we have been well aware of for some time, we can theorize about how incrementally effective this basket would have been relative to IWV had we shorted it at the beginning of March. We can see that the custom basket would have underperformed the ETF for the entirety of the month, ending at -0.45% as of 3/25 versus IWV’s 1.74%.


As expected, when looking through the lens of the Wolfe US Broad model, Interest Rate Beta is the largest driver. However, we also see advantageous factor drags from Growth, Book to Price, Earnings Yield, Profitability, and Leverage.


Using this framework, we can not only build better factor-driven baskets, but we can also use a risk-aware optimizer to even better fit them to any given portfolio. An optimizer can help identify the right combination of stocks and position sizes to manufacture a basket that coincides with our factor research and a portfolio’s existing risks to deliver a hedge that goes far beyond what an ETF can do.

If you are interested in exploring hedge basket creation through Omega Point, please do not hesitate to reach out to us.

US & Global Market Summary 

US Market: 03/22/21 - 03/26/21Screen Shot 2021-03-27 at 2.36.19 PM.png

  • The US market saw continued volatility, bottoming out on Wednesday and then surging back on Friday to end up on the week (the S&P 500 closed the week at +1.6%).
  • Investor sentiment was boosted by the doubling of the US vaccine rollout target (to 200 million shots in President Biden’s first 100 days), some positive economic data, and the Fed’s move to unblock banks from raising dividends and buybacks.
  • U Michigan’s final consumer sentiment index reading for March was up to 84.9 from an earlier reading of 83, reaching its highest level since March 2020.
  • The Labor Department reported 684,000 jobless claims last week, significantly lower than the previous week’s 781,000 and the first time claims were lower than 700,000 since the pandemic began.
  • The Commerce Department also reported that 4Q GDP came in stronger than expected at +4.3% (vs. the previous estimate of 4.1%), driven by stronger inventory restocking by US businesses. 2020 GDP still shrank by -3.5%.

Normalized Factor Returns: Axioma US Equity Risk Model (AXUS4-MH)Screen Shot 2021-03-27 at 8.02.15 PM
Methodology for normalized factor returns

  • Size was again the week’s biggest winner, up another 0.46 standard deviations and poised to become an Overbought factor at +0.99 SD above the mean.
  • Growth bounced from a nadir of -2.44 SD below the mean on 3/18 to -2.12 SD below the mean on Friday, and is on the cusp of breaking out of Extremely Oversold territory.
  • Profitability and Earnings Yield saw a slight uptick in normalized return and are both approaching Overbought space.
  • Value has started to revert after touching a recent pinnacle of +2.01 SD above the mean on 3/22, shedding its Extremely Overbought designation as it now sits +1.8 SD above the mean.
  • Volatility reversed course after a slight upwards move last week, and is now -1.84 SD below the mean.
  • Market Sensitivity plummeted by 0.43 standard deviations and is now close to historical trend at +0.05 above the mean.
  • US Total Risk (using the Russell 3000 as proxy) saw a notable increase of 42 basis points.

Normalized Factor Returns: Axioma Worldwide Equity Risk Model (AXWW4-MH)
Screen Shot 2021-03-27 at 4.07.05 PM.png
Methodology for normalized factor returns

  • Profitability enjoyed a +0.31 standard deviation boost on a normalized basis, and is now +0.67 SD above the mean.
  • Similar to the US, Growth rebounded from a trough of -2.36 SD below the mean on 3/22 and is on the verge of exiting Extremely Oversold space.
  • Earnings Yield continued to climb higher into Extremely Overbought space, now +2.62 SD above the mean.
  • Value hit a recent peak of +1.37 SD above the mean on 3/16, and has now declined enough to shed its Overbought designation.
  • Market Sensitivity and Volatility both sold off on a normalized basis, as Volatility became an Oversold factor at -1.29 SD below the mean.
  • Global Risk (using the ACWI as proxy) saw a 22bps increase.