Omega Point Blog

Marrying Macro and Fundamental Models

Chris Martin

Marrying Macro and Fundamental Models

May 02, 2021

Hi there,

Last week, we established that Macroeconomic factors appear to show a meaningful explanatory power over sectors such as Energy, Financials, and Real Estate. We concluded our analysis by noting that incorporating macro factors into the investment process shouldn’t be disruptive, but can actually be supportive. This week, we continue our examination to demonstrate how both macro & fundamental factors can indeed co-exist and provide investors a meaningful view into both how and when ‘macro matters’. We will leverage both the Axioma WW Fundamental and Axioma WW Macro Projection Models in our analysis.

Also, if you would like to see firsthand how macro is impacting your own porfolio, we encourage you to sign up for a complimentary portfolio evaluation.

Global Fundamental Model vs. Global Macro Projection Model

Here’s a view of the Predicted Risk of the Russell 1000 through the lens of both the Axioma WW Fundamental and WW Macro Projection model:


To first quash any misunderstanding - no, we did not forget to add the Fundamental model’s data to the above chart. The predicted risk of IWB is almost exactly the same in both models.

You may be scratching your head right now. What’s the point of having a macro model if it doesn’t offer a different risk lens than the fundamental equity model?

The short answer is the Macro Projection isn’t necessarily about building a different risk estimate nor trying to explain more factor risk than the WW model, rather it is a reallocation risk to observable macroeconomic data in global markets. So if our predicted risks are the same, how do the macro factors reallocate our predicted factor risks?

What Risks Do the Macro Model Explain?

Consider computing the following metric for each factor category (Market, Style, Sector, Country, Currency, Macro) in the model:

  • Take the % of Factor risk in that category coming from the Fundamental model and subtract the % of Factor risk in that category coming from the Macro Projection Model.

When the % of risk is above the 0 line, the pure Fundamental model explains more of that category’s Factor variance, and when the % of risk is below the 0% line the Macro Projection model explains more of that category’s Factor variance.

For the Russell 1000 chart shown above, we observe that for most categories the Fundamental & Macro models predict similar amounts of factor risk.

Except for....the Global Market and Macro factors.

In fact, they appear to be mirror images of each other.

The key takeaway here is that the Macro factor overlay in the WW Macro Projection model helps explain the Fundamental Model’s Global Market’s behavior and risk. While the whims of the broad market can be difficult to predict and understand, adding Macro factors can help us disaggregate the drivers of the overall Market returns and risks.

Let’s take a look at the MSCI Emerging Markets ETF:

image.pngOnce again we see that the Macro Projection model helps explain more of the Global Market factor, but we also see that the Country factors also explain more risk in the macro model. Given Emerging Markets tend to be more natural resource export oriented, it makes sense that the Macro Projection model helps explain a bit more of these Country factors.

Macro Risk Matters, But When?

Extrapolating insights from charts of differences, we return to looking at the Macro Projection model on its own, specifically the Predicted % of Variance drivers since 2007. If we follow the red line (Macro risk%), we see two peaks:

  • Summer 2012 — During the height of the EU sovereign debt crisis
  • Mar/Apr 2020 — During the initial shock of the pandemic


Now let’s take a look at realized performance attribution from the Macro Projection Model.

Below is a breakdown of the realized return of IWB, starting at the end of March 2020 just as the market was starting to recover from the COVID crash. Through the lens of the Macro Projection model, macro factors have been the main driver of returns.


Perhaps this is an interesting nuance of the Macro Projection model? Have Macro factors always driven performance? We rewind the clock back to 2007 to give the above chart a sanity check:


Historically, Macro Factors have certainly had meaningful impact on returns for IWB, and COVID took it to another level. The drop during COVID was driven by widening Credit spreads in the US and the UK as fears of corporate viability during a prolonged pandemic started hitting the market:


But just as Macro factors taketh, so doth they giveth. US Credit spreads have recovered, likely driven by the aggressive monetary and fiscal policies enacted by both the Fed and the US government. We see that the main sources of return helping drive markets are now driven by non-Oil Commodity prices and Inflation.


The ability to add Macro Factors into our risk model provides more insight into what is driving our portfolio risks and returns, which in turns helps us make better investment decisions. Whether the goal is to build a thematic basket to capture expected Macro moves or hedging undesired/unexpected Macro risks, the increased factor granularity beyond the ‘Global Market’ helps us accomplish our goals.

As a reminder, if you are interested in exploring how the macro factors that we’ve discussed are impacting your portfolio’s risks and returns, please don’t hesitate to sign up for a complimentary ‘macro matters’ portfolio evaluation.

US & Global Market Summary 

US Market: 04/26/21 - 04/30/21

Screen Shot 2021-05-01 at 3.01.14 PM.png

  • The US market was flat to slightly up for most of the week, then saw a broad selloff on Friday - presumably driven by profit taking after a week of strong earnings reports and encouraging economic data.
  • The S&P 500 and NASDAQ both closed the month of April up more than 5%, while the DJI ended the month +2.7%.
  • 60% of the S&P 500 has now reported, with consensus beats inching towards record highs across all sectors (86% beat EPS projections). Earnings are now expected to be up +46% in 1Q21 vs. 1Q20, compared to earlier forecasts of +24%.
  • Fed Chair Powell made sanguine comments about the economy on Wednesday, but ultimately gave no indication of a change in monetary policy in the near term.
  • Our first look at 1Q GDP showed an expansion of +6.4%, beating estimates of a +6.1% annualized rate.
  • The Labor Department reported 553,000 new weekly jobless claims, notching another new pandemic low.
  • On Friday, the US announced that it would bar most travel to and from India beginning next Tuesday, as India struggles through the throes of a massive second wave of COVID infections.

Normalized Factor Returns: Axioma US Equity Risk Model (AXUS4-MH)
Screen Shot 2021-05-01 at 4.16.45 PM.png

Methodology for normalized factor returns

  • Volatility was the week’s biggest winner, as it continued to rebound from a recent 4/20 trough of -2.10 SD below the mean.
  • Momentum saw positive normalized gains for the 5th straight week, now sitting at +0.61 SD above the mean.
  • Growth continued its climb towards Extremely Overbought territory, now at +1.78 SD above the mean.
  • Size continued to slide down from a 4/20 peak of +2.08 SD above the mean, exiting Extremely Overbought space as i now sits at +1.86 SD above the mean.
  • Profitability tumbled out of Oversold space, as it now sits at +0.57 SD above the mean.
  • Sustained weakness in Value continued, as it nears an Extremely Oversold label at -1.72 SD below the mean.
  • US Total Risk (using the Russell 3000 as proxy) saw a significant drop of 1%.

Normalized Factor Returns: Axioma Worldwide Equity Risk Model (AXWW4-MH)
Screen Shot 2021-05-01 at 4.18.56 PM.png
Methodology for normalized factor returns

  • Similar to the US, Volatility saw a sizable upwards move as it continued to recover from a 4/20 trough of -1.94 SD below the mean.
  • Ongoing strength in Momentum has led it to the doorstep of garnering an Overbought designation, as it now sits at +0.96 SD above the mean.
  • Value declined by 0.13 standard deviations, and is approaching an Extremely Oversold label at -1.8 SD below the mean.
  • Profitability fell out of Extremely Overbought space, now at 1.97 SD above the mean after hitting a recent peak of +2.41 Sd above the mean on 4/21.
  • Earnings Yield fell again and sits much further away from it Extremely Overbought status seen just 3 weeks prior.
  • Global Risk (using the ACWI as proxy) saw another significant decline of 69 bps.