Factor Spotlight
Factor University

An Energy-First View of the World

Before we kick off this week’s edition, we’d like to take a brief moment to send our thoughts and wishes to the millions of Texans, which include our customers and several members of Omega Point’s team, who are struggling with freezing temperatures, power outages and disrupted water service. This is a crisis of epic proportions, especially to those most vulnerable, and it is our hope that yesterday’s disaster declaration by President Biden will expedite financial aid and temporary housing to ease the burdens of those who are suffering.

With those thoughts heavy in mind, this week we take a closer look at the energy sector and lay foundations for future Factor Spotlights focused on the material risks of climate-related events such as what occurred in Texas this past week.

To help us along, we continue our deep dive into Wolfe Research’s powerful array of sector-focused risk models. As we’ve recently shown, using the Wolfe US TMT model as an example, sector models allow managers to better quantify the unique factors that are driving risk and performance in their portfolio, which can otherwise be washed out when using a broad market risk model.

Our focus today is on the Wolfe Research QES US Energy (“Wolfe US Energy”) risk model.Given the state of emergency in Texas that has left so many without power, heat, or water and the resulting supply squeeze sending the price of natural gas skyrocketing (and subsequently plummeting), our highlight of this model is intended to be both timely and actionable. The energy-specific model factors should help investors better understand the potential fallout (and opportunities) in their portfolio as a result of these energy-related market events.

Methodology & Motivation for the Wolfe US Energy Risk Model

One key aspect of the Wolfe US Energy risk model is that it is constructed using an estimation universe of US all-cap energy securities, as opposed to using a broad US universe, as is the case with traditional broad market risk models. In addition to factors that are common across other sector and broad market models, the energy-specific model includes several factors relevant for understanding the behavior of the energy sector. This covers fundamental factors, such as Free Cash Flow Yield and EBITDA-to-EV, macro factors, such as sensitivity to Oil Prices, Natural Gas Prices, and Interest Rates, and granular industry factors, such as Refiners, Coal, and Pipelines.

While the model’s estimation universe is the energy sector, the model is able to cover all US equities due to the time series regression methodology used to estimate the security sensitivities to the factors. Please see the risk model factsheet for more information on the construction and factors used in this model.

To run our comparison of the energy model to a broad market risk model, we’ll be using the Wolfe Research QES US Broad (“Wolfe US Broad”) risk model. The estimation universe for the broad market risk model represents a US all-cap universe that covers all sectors and the energy sector is severely underrepresented in this universe. Energy securities make up only about 3.5% of the Russell 3000 - proxied using the iShares Russell 3000 ETF (IWV) - based on number of securities, and only 2.4% based on market value.

Sector Breakdown for iShares Russell 3000 ETF (IWV)

Source: iShares

Broad market risk models, while very useful for portfolios diversified across sectors, tend to have “coarse” industry factors and in the case of energy, typically only include 1-2 factors that are able to pick up on the nuances of energy’s industry dynamics. For example, the Wolfe US Broad risk model only includes 2 broad energy factors, whereas the Wolfe US Energy risk model includes 6 granular energy factors.

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Given this, it makes sense that managers and analysts focused on the energy space would find immense value from quantifying the factor exposures of their securities relative to a model that is representative of their sector.

Taking an Energy-First Perspective

Investors who are looking to understand the unique dynamics of the energy market can extract vastly more meaningful information from an "energy-first" view of the world vs. the more common information available in broader models.

Focusing more closely on the Oil Beta factor allows us to illustrate this concept since this macro-economic factor exists in both the Wolfe US Broad and Wolfe US Energy risk models. When viewing the factor profile for the Oil Beta factor over the past 3 months, we see a few stark differences between this factor in the broad vs energy-specific model.

Oil Beta - Wolfe US Broad Risk Model


Oil Beta - Wolfe US Energy Risk Model


Looking at the performance of this factor in each model, we can see that the Oil Beta factor performs quite differently. In the broad risk model, the 3-month factor return is barely above 0.4% and hits a cumulative high of 1% on Jan 13, 2021. In contrast, the 3-month factor return in the energy risk model is over 5%, with a cumulative high of almost 9% on Jan 11, 2021. This tells us that the performance of Oil Beta within the energy universe has a much different behavior than the performance in the broad market universe. Using this factor from the broad model as an indicator for the return of securities with high sensitivity to oil prices can be misleading within the energy universe.

Perhaps more important than the return profile is the exposure that this factor has to the energy universe in each model. In the factor profiles above, we see that the VanEck Vectors Oil Services ETF (OIH), has vastly different exposures to the Oil Beta factor in each model. The Wolfe US Broad model would tell us the obvious - OIH has a huge sensitivity to oil prices. Given that this ETF covers oil-focused stocks, this comes as no surprise and is information that does not require a risk model to understand. However, the Wolfe US Energy model tells us that within the context of the energy universe, OIH has a much more moderate sensitivity to oil fluctuations.

Zooming in on OIH’s other style factors shows that while the broad risk model assess Oil Beta as the largest factor exposure for the ETF, the energy risk model highlights other exposures that we may have otherwise overlooked.

OIH’s Style Factor Exposures - Wolfe US Broad Risk Model


OIH’s Style Factor Exposures - Wolfe US Energy Risk Model


In the Wolfe US Energy risk model, Oil Beta is the sixth largest style factor exposure and the model highlights that the ETF is much more exposed to the Size and ETF Crowding factors. We also can glean other meaningful conclusions, such as noting that OIH is not actually that sensitive to Interest Rate Beta or Natural Gas Prices when viewing the ETF in the context of other energy securities.

Looking Past the Obvious with an Energy Model

If you’re a portfolio manager or analyst focused on the energy sector, you likely find it challenging to work with traditional broad market models because these models tell you information that you already know. Seeing that your energy universe is highly exposed to macro factors across the board may not be very insightful for you or help you to differentiate between the names in your coverage list.

However, understanding the exposure to macro factors relative to other energy namescan provide you with a mechanism to better recognize nuances across the energy universe. Additionally, the energy-specific risk model can help you to screen your names for other relevant factor exposures in the context of the energy universe. For energy sector-specialists or for managers that run portfolios with heavy energy sector exposure, the Wolfe US Energy risk model can provide a high degree of transparency and give you a more realistic sense of the factors that truly drive risk and performance in the portfolio.

US & Global Market Summary

US Market: 02/15/21 - 02/19/21

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US Stock Market Cumulative Return: 2/15/2021 - 2/19/2021
  • The market took a break from hitting record highs and ended the holiday-truncated week lower, primarily driven by burgeoning investor fears of higher inflation and rising interest rates, as well as some profit-taking in the tech sector.
  • Yield on the 10-Year Treasury reached levels not seen in almost a year hitting 1.35% on Friday.
  • US crude oil exceeded $60 a barrel at one point this week as Texas and the country as a whole dealt with harsh winter weather.
  • House Democrats are focused on passing a $1.9T relief plan before the end of February, as the number of Americans applying for jobless aid (861,000) rose last week by 13,000 and reversing several weeks of steady declines.
  • Closed sales of existing homes increased by 0.6% in January after a brief pullback in December, according to NARA, despite record low supply.

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

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Methodology for normalized factor returns
  • Size was the biggest winner this week after exiting Oversold space last week, up +0.23 standard deviations.
  • Value saw a solid positive move, up +0.19 SD on its way towards the mean.
  • Market Sensitivity shed its Oversold label after a +0.1 SD move, now sitting at -0.93 SD below the mean.
  • Earnings Yield and Volatility both saw slight declines after being tied for biggest winner last week.
  • Momentum continued to drift down as it remains an Overbought factor.
  • Growth was again the biggest loser on the week, and is no longer an Overbought factor as it still sits +0.93 SD above the mean. Growth hit a recent peak of +1.49 SD above the mean on Feb 5.
  • US Total Risk (using the Russell 3000 as proxy) decreased by 20bps.

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

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Methodology for normalized factor returns

  • Size was the biggest winner globally as well as in the US model, up +0.29 SD and headed back towards the mean.
  • Value also saw a decent normalized gain, up +0.25 standard deviations.
  • Earnings Yield is climbing back towards the mean after losing an Oversold label last week.
  • Exchange Rate Sensitivity continued to slowly drift down looks poised to cross into Oversold territory.
  • Profitability declined further into negative normalized space after sitting at the mean last week.
  • Momentum saw the week’s biggest move down, falling by 0.32 standard deviations and looking close to leaving Overbought space at +1.07 SD above the mean.
  • Global Risk (using the ACWI as proxy) declined by 32bps.


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