Macro Alpha Primer: Correlations

Nominal GDP and Real GDP as it relates to the stock bond correlation

We will continue to move forward with the Macro Alpha Primers. I already wrote the first one on credit risk and duration risk with a connected podcast:

• Macro Alpha Primer: Credit Risk and Duration Risk

• Macro Podcast: Macro Alpha Primer

We are going to build on this by breaking down HOW and WHY correlations connect to the macro regime. We will start very basic with the stock-bond correlation, why it matters, and the connected trades you can run when you understand these correlations.

As you know, all the previous educational primers on every aspect of macro are here:

“You wasted $150,000 on an education you coulda got for $1.50 in late fees at the public library.”

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Big Picture Logic:

Why do correlations matter? I want to start by going over several points on this.

• First, BOTH outright and relative relationships in financial markets send signals. Equities rallying or falling send a one-dimensional signal but you can begin creating multi-dimensional signals when you overlay relative relationships.

• Second, managers and investors are always looking at their overall portfolio as opposed to siloed parts. A specific correlation between assets can significantly boost or drag on portfolio returns. For example, during 2022 investors had a higher sensitivity to losses because BOTH parts of their portfolios were in a drawdown (stocks and bonds) which was in some ways worse than the 2020 COVID crash when the stock-bond correlation was negative and positions helped offset each other.

• Third, correlations matter in a macro context because no asset is the same or has the same exposure to the macro regime. Fundamentally, every asset in financial markets has a unique sensitivity to the growth, inflation, and liquidity within the system. There is a reason assets are not all the same thing and have divergences or convergences in varying degrees of strength.

If you want to spend time thinking about these dynamics on a first-principles basis, I would encourage you to read The Origin of Wealth (link). This is an exceptional book that has been instrumental in my thinking about modeling complex systems.

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Macro GDP:

As I noted in the first primer (link), real and nominal GDP determine credit risk and duration risk.

“When we move through a typical quarter of economic data, there are both nominal changes and real changes. For example, here is a chart of nominal and real GDP. When mapping these into quantifiable regimes, you want to map the level, rate of change, and spread between these two data points. When you have mapped these data points, then you will have a clearer view into credit risk and duration risk in financial markets.”

-First Macro Alpha Primer

The scenario distribution of growth, inflation, and liquidity determines the correlation between stocks and bonds.

• Growth Expanding, Inflation Rising, Liquidity Expanding

• Growth Expanding, Inflation Rising, Liquidity Contracting

• Growth Expanding, Inflation Falling, Liquidity Expanding

• Growth Expanding, Inflation Falling, Liquidity Contracting

• Growth Contracting, Inflation Rising, Liquidity Expanding

• Growth Contracting, Inflation Rising, Liquidity Contracting

• Growth Contracting, Inflation Falling, Liquidity Expanding

• Growth Contracting, Inflation Falling, Liquidity Contracting

The “problem” to solve for in the scenarios above is that there are always varying degrees of strength in each of these scenarios. Additionally, there is fundamental uncertainty about what the current regime is and what the future regime will be. This brings us to the three-body problem:

The Three-Body Problem is a classic problem in physics and mathematics that involves predicting the motion of three celestial bodies interacting with each other through gravity. Unlike the two-body problem, where the motion of two bodies can be solved with precise equations, the three-body problem is highly complex and chaotic, meaning that even slight changes in initial conditions can lead to vastly different outcomes. This problem illustrates the inherent uncertainty and unpredictability in systems with three or more interacting bodies, as their interactions create a dynamic environment where long-term predictions become nearly impossible.

Logic For Correlation:

The correlations between stocks and bonds exist because the same input is driving a specific attribution of both stocks and bonds at the same time. This is key for identifying correlations: You need to map the existence of the correlation with the specific attribution analysis (this is temporally dependent).

How would you do this? Go through the S&P500 primer (link) and interest rate primer (link, link) to understand which aspects of growth, inflation, and liquidity drive the specific attribution. Several points on this:

• Inflation is always going to be priced by inflation swaps and the breakeven component of the bond market. Even during 2021 when the Fed held rates below inflation (as reflected in the 2 year in blue), 10 year inflation swaps rallied and caused long rates to reprice.

• Connecting the short end and the long end with how the Fed is targeting inflation is going to generate your view of the curve. I had an entire conversation with

  Prometheus Research
  on this: Link. Growth is going to determine how restrictive the Fed is able to be. For example, in 2023 the Fed held the Fed Funds rate well above inflation and targeted a higher spread due to the resilience in growth. In other words, the spread between Fed Funds and Inflation is determined by HOW the Fed is viewing growth.

• As you begin to contextualize how growth, inflation, and liquidity impact interest rates across the curve, then you can begin to understand how growth, inflation, and liquidity impact equities. When these scenarios overlap at the same time, positive or negative correlations take place.

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Examples:

We can go through some very simple examples of the stock-bond correlation

• 2020 saw a negative stock-bond correlation as the initial crash happened due to a recession and deflation. This positioning unwound on a cyclical basis which is why the negative correlation remained.

• 2022 saw the correlation flip noticeably positive as the primary driver was the negative liquidity impulse from the Fed. This positive correlation remained through the end of 2023 and 2024 because the regime has been one of Goldilocks.

These two the two primary examples over the last 5 years but all of the small moves in between become increasingly difficult to map without a very robust view of growth, inflation and liquidity. This is why the educational primers I wrote on macro (link) function as a starting point for understanding these.

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Resources and Portfolio Sensitivity to Losses:

The stock-bond correlation is a topic of heightened focus in portfolio management because portfolios are sensitive to total P&L changes. Holding stocks and bonds is supposed to (theoretically) provide diversification benefits through various regimes. When multiple parts of a portfolio contribute negatively, this causes greater drawdowns than are expected for investors and if there is some type of volatility control in the portfolio then the manager is forced to sell multiple assets simultaneously.

This is actually one of the things that caused stocks and bonds to sell off so aggressively during 2022. Portfolios that held stocks and bonds with vol control were forced to sell BOTH. This idea of sensitivity to losses is incredibly important to think about from a trading perspective because there are a multiplicity of investors in financial markets who cross-collateralized their exposure.

A great book to start with on this topic is Beyond Diversification: Link

The following papers provide a great breakdown of this topic as well:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3075816

https://www.bis.org/publ/qtrpdf/r_qt2312v.htm

https://www.aqr.com/Insights/Research/Journal-Article/A-Changing-Stock-Bond-Correlation

https://www.aqr.com/Insights/Research/White-Papers/When-Stock-Bond-Diversification-Fails

https://www.aqr.com/Insights/Research/Journal-Article/Stock-Bond-Correlations

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Trades:

If you begin to frame the correlations for stocks and bonds with macro, then you can begin constructing trades that have more optionality in multiple futures scenarios. Here are the main scenarios:

• Long Stocks

• Short Stocks

• Long Bonds

• Short Bonds

• Long Stocks / Short Bonds

• Short Stocks / Long Bonds

• Long Stocks / Long Bonds

• Short Stocks / Short Bonds

While these are the main ones, the exposure to each can be changed dramatically or volatility-weighted in order to have specific contributions to the overall P&L. Many times being long or short both stocks and bonds on a volatility-weighted basis has a higher probability of making money than being long or short only one of them.

Even if you aren’t actively trading any of these, watching the correlation and ratios between stocks and bonds provides an incredibly important signal for where we are in the macrocycle.

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Bringing Things Together:

One of the things I have brought up consistently is that the days when you could just hold stocks and bonds blindly are over. 2022 woke investors up to the reality that BOTH can fall at the same time. When multiple pillars of a portfolio fall at the same time, it can create drawdowns greater than what might occur during a recession or credit event. The only way to manage these types of regimes is active management. The majority of the industry can’t even fathom the idea of shorting bonds and yet that was one of the primary ways of generating returns in 2022.

The implication of this is that those managers, financial advisors, and traders who prepare for these scenarios will be rewarded with greater amounts of AUM. Shocks to the financial industry will continue to occur until greater efficiency is achieved.

Thanks for reading

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A Pepe for the culture!

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