On the Brink?
How to contextualize last week's market moves
By: Chris Satterthwaite, Lionel Smoler Schatz, and Oleg Laskov
Equity volatility spiked last week to levels not seen since COVID. Our favorite macroeconomic indicator, the high-yield spread, increased suddenly. And the stock-bond correlation dropped as Treasurys rallied while equities fell, breaking with our recent experience during inflation when the two asset classes tended to sell off together.
Charting the Euclidean distance from Monday, when volatility peaked, to previous moments in market history, we can see that the closest historical analogies are the dot-com bubble (’99-’00), the Great Financial Crisis (’07-08’), COVID lows (2020). and the 2022 tech meltdown.
Figure 1: Historical “Euclidean Distance” to August 5, 2024
Source: Verdad analysis
With these types of macro signals, it’s no surprise that we saw significant deleveraging of investor portfolios. The most common way hedge funds manage risk is to reduce exposure when volatility goes up. This reaction function can amplify market moves.
We can also contextualize last week’s market movements in the context of the regime model we described last week. Prior to the volatility spike, the market looked to be in the Precarious regime, which is defined by risk-taking behavior (compressed high-yield spreads), depressed volatility, a flat or inverted yield curve, and increasing stock-bond correlation. By Monday, the market was clearly in a Crisis regime, defined by high risk aversion and significantly elevated volatility. And by the end of last week, volatility came down and the market was back in a Precarious regime.
Below, we can see how each of the market regimes compares to Thursday, Friday, and Monday’s economic signals.
Figure 2: Average Regime Signals vs. August 2024
Source: Verdad analysis
The power of a framework like this is that it allows for responsive, real-time contextualization of a swatch of economic data.
These signals, however, are not perfect. We use them to try to predict the future macroeconomic climate and thus asset class performance. But we are challenged both by the volatility of these signals—how rapidly they shift from one regime to another—and by their imperfect predictive ability—which is prone to false positives.
This leaves investors with two potential courses. The first is to be a long-term investor and ignore these signals entirely, knowing that volatility is the price to pay for long-term equity returns. The second is to react to these fast-moving signals in order to manage volatility and attempt to make smart short-term forecasts based on rigorous study of the data.
Acknowledgement: Our intern Oleg Laskov has been working on this research. He is a rising junior at Yale University majoring in applied mathematics. He is interested in quantitative finance, trading, and machine learning.