The 42 Macro “Playbook”

GRID Regime

Short for GOLDILOCKS, REFLATION, INFLATION, and DEFLATION, GRID Regimes are the backbone of our macro risk management process. The GRID Regimes are valuable because they help investors quickly orient their investment strategy by communicating where and how much to allocate capital within and across asset classes. GOLDILOCKS and REFLATION are both indicative of “risk on” market conditions – the former with a disinflationary bias and the latter with an inflationary bias. INFLATION and DEFLATION are both indicative of “risk off” market conditions – the former with an inflationary bias and the latter with a disinflationary bias.

Growth

The change in the level of economic activity as measured by publicly available and/or widely followed periodic indicators like GDP, Industrial Production, Consumer Spending, PMIs, etc. Our process anchors on the OECD Composite Leading Index data because the methodology is consistent across geographies. Moreover, because the OECD Composite Leading Index data are released monthly, this indicator allows investors to confirm and react to inflections in the trending rate of change of economic growth much faster than quarterly GDP statistics.

Inflation

The change in the level of prices as measured by publicly available and/or widely followed periodic indicators like the Consumer Price Index, Producer Price Index, Personal Consumption Expenditures Price Index, etc. Our process anchors on Headline CPI because that statistic tends to have the most predictive value with respect to forecasting inflections and/or persistence in the momentum of key financial market indicators like interest rates.

Sectors

The grouping of stocks and credits according to how the businesses make money, which itself is a function of the products they sell and the customers they serve.

Style Factors

The grouping of stocks and credits according to the characteristics of securities themselves, which are usually in relation to a larger universe. For example, High Beta stocks are stocks that have a beta in excess of the mean or median beta of the broader market, insomuch that High Yield stocks are stocks that have a dividend (or free cash flow) yield in excess of the mean or median dividend (or free cash flow) yield of the broader market.

Bottom-Up Macro Regime

The characterization of an economy according to the trending rates of change of growth and inflation. For the purposes of our GRID Regime process, we define the trending rates of change as the trailing 3-month level change of the OECD Composite Leading Index and the trailing 3-month level change of Headline CPI YoY. If the trailing 3-month level change of the OECD Composite Leading Index is positive and the trailing 3-month level change of Headline CPI YoY is negative, an economy is said to have been in GOLDILOCKS for that month. If the trailing 3-month level change of the OECD Composite Leading Index is positive and the trailing 3-month level change of Headline CPI YoY is positive, an economy is said to have been in REFLATION for that month. If the trailing 3-month level change of the OECD Composite Leading Index is negative and the trailing 3-month level change of Headline CPI YoY is positive, an economy is said to have been in INFLATION for that month. If the trailing 3-month level change of the OECD Composite Leading Index is negative and the trailing 3-month level change of Headline CPI YoY is negative, an economy is said to have been in DEFLATION for that month.

Conditional Probability

The likelihood of an economy realizing a particular GRID Regime in a given month according to the direction and magnitude of the realized or projected deltas in growth and inflation relative to the standard error of our growth and inflation models for the particular geography. For every economy we maintain GRID Models for, we employ a combination of two independent models – our Stationary Mean Reversion Model and our Agent-Based Nowcast Model – to project both growth and inflation simultaneously. Our official growth and inflation projections are derived from the median of those two forecasts.

Top-Down Market Regime

The characterization of global financial markets according to the trending momentum of key macro market indicators, relative to how they have historically traded during periods of Bottom-Up Macro Regime GOLDILOCKS, REFLATION, INFLATION, and DEFLATION. Our Top-Down Market Regime nowcasting process is valuable because the consistent identification of the GRID Regime helps investors quickly orient their investment strategy by communicating where and how much to allocate capital within and across asset classes. GOLDILOCKS and REFLATION are both indicative of “risk on” market conditions – the former with a disinflationary bias and the latter with an inflationary bias. INFLATION and DEFLATION are both indicative of “risk off” market conditions – the former with an inflationary bias and the latter with a disinflationary bias. Our investment process anchors on the current and subsequent Top-Down Market Regimes as the key driver of the risks we take in the 42 Macro Portfolio Construction.

Volatility-Adjusted Momentum Signal (VAMS)

The primary tool we use to track momentum in asset markets. The volatility adjustment is valuable because it tends to be a leading indicator for breakouts or breakdowns in medium-term price momentum. The model itself is a dimension-reduced dynamic factor model based on a cumulative scoring system whereby an asset receives -2pts (bearish VAMS), 0pts (neutral VAMS), +2pts (bullish VAMS) via a -1pt/+1pt contribution if the short-term moving average of price is below/above its medium-term moving average and via a -1pt/+1pt contribution if the short-term moving average of volatility is above/below the median of the corresponding medium-term sample – or vice versa, depending on the asset’s historical correlation to its implied or realized volatility. For example, interest rates, cryptocurrencies, and implied volatility indices tend be positively correlated with their respective volatilities, whereas equities, credit, and commodities tend to be inversely correlated.

Global Macro Risk Matrix

The intermarket analysis model we use to nowcast the Top-Down Market Regime, which is a Bayesian inference process that we repeat daily across 42 of the world’s most important macro market indicators spanning a dozen liquid asset classes. Each bullish VAMS reading or bearish VAMS reading reading in our Global Macro Risk Matrix adds +1pt to each GRID Regime the signal makes sense in from the perspective of how that exposure has historically traded in each regime. For example, a bullish VAMS reading in the S&P 500 would add +1pt to GOLDILOCKS and +1pt to REFLATION, as each is a “risk on” regime in our GRID Regime framework. Conversely, a bearish VAMS reading in the S&P 500 would add +1pt to INFLATION and +1pt to DEFLATION, as each is a “risk off” regime in our GRID Regime framework. Neutral VAMS readings do not contribute to the scores of either GRID Regime.

Sum of Confirming Markets

The number of macro market indicators featured in our Global Macro Risk Matrix that are confirming of each GRID Regime at each daily interval. This metric is valuable because it allows investors to quantify the relative influence of each GRID Regime upon the evolution of market narratives, changes in investor behavior, and, most importantly, the relative and absolute performance of asset markets themselves.

Conviction Score

We display the cumulative Sum of Confirming Markets across the four GRID Regimes on a percentile basis since the start of 1998. The metric is valuable because it allows investors to determine how strong of a “risk on” or “risk off” signal investors are receiving from the key macro market indicators featured in our Global Macro Risk Matrix. We consider readings in the lower quintile to be depressed and indicative of a high degree of uncertainty being priced into asset markets. We apply a reflexivity framework to further interpret that implied uncertainty as suggestive of consensus positioning for the highest scoring GRID Regime(s) as being depressed from the perspectives of leverage and crowding. We consider readings in the upper quintile to be elevated and indicative of a high degree of certainty being priced into asset markets. We apply a reflexivity framework to further interpret that implied certainty as suggestive of consensus positioning for the highest scoring GRID Regime(s) as being elevated from the perspectives of leverage and crowding.

Share of Confirming Markets

Each individual GRID Regime’s Sum of Confirming Markets divided by the total Sum of Confirming Markets across all four GRID Regimes at each daily interval. This metric is valuable because it allows investors to quantify the relative influence of each GRID Regime upon the evolution of market narratives, changes in investor behavior, and, most importantly, the relative and absolute performance of asset markets themselves.

Dominant Market Regime

The GRID Regime with the highest Share of Confirming Markets at each daily interval is said to be the Dominant Market Regime. The Dominant Market Regime is very important to our investment process because the degree of risk we take and types of exposures featured in the 42 Macro Portfolio Construction are both direct functions of A) the current Dominant Market Regime and B) which GRID Regime we believe is most likely to become the next Dominant Market Regime based upon our Bottom-Up Macro Regime projections for the US and Global economies over the pending 3-6 months.

Cross-Asset Correction Risk Indicator (CACRI)

CACRI is our primary tool for quantifying the degree of downside risk embedded in risk assets over the near-term. CACRI is the sum of the respective Shares of Confirming Markets of INFLATION and DEFLATION – the two “risk off” GRID Regimes in our Top-Down Market Regime nowcasting process. CACRI is valuable to investors because it is consistent. Specifically, the median CACRI reading for the 12 eight-plus percent S&P 500 corrections in our post-GFC sample is 15% with an interquartile range of 900bps. As such, CACRI begins to signal rising risk of a meaningful correction whenever it falls into the low-20s/high-teens. From a flows perspective, we interpret such depressed CACRI readings as evidence of the momentum strategies employed by CTAs, Quants, and Macro Funds having reached peak/near-peak leverage, insomuch that it also signals an unhealthy degree of complacency among fundamentally oriented investors. Investors should be actively booking gains in risk assets whenever CACRI begins to recover from such depressed readings.

VVIX/VIX Ratio

The level of the Cboe Volatility of Volatility Index divided by the level of the Cboe S&P 500 Volatility Index. This intramarket analysis metric is valuable for two reasons: 1) because it often breaks out to a bullish VAMS state ahead of a Dominant Market Regime phase transition to GOLDILOCKS or REFLATION – the two “risk on” GRID Regimes in our framework; and 2) because it often breaks down to a bearish VAMS state ahead of a Dominant Market Regime phase transition to INFLATION or DEFLATION – the two “risk off” GRID Regimes in our framework. The VVIX/VIX Ratio is at the leading edge of our process for assessing and proactively positioning for changing market conditions and, thus, is one of our “Four Horsemen of Market Risk”.

High Beta/Low Beta Ratio

The level of the S&P 500 High Beta Index – which tracks the performance of the 100 most volatile stocks in the S&P 500 over the trailing 252 trading days – divided by the level of the S&P 500 Low Volatility Index – which tracks the performance of the 100 least volatile stocks in the S&P 500 over the trailing 252 trading days. This intramarket analysis metric is valuable for two reasons: 1) because it often breaks out to a bullish VAMS state ahead of a Dominant Market Regime phase transition to GOLDILOCKS or REFLATION – the two “risk on” GRID Regimes in our framework; and 2) because it often breaks down to a bearish VAMS state ahead of a Dominant Market Regime phase transition to INFLATION or DEFLATION – the two “risk off” GRID Regimes in our framework. The High Beta/Low Beta Ratio is at the leading edge of our process for assessing and proactively positioning for changing market conditions and, thus, is one of our “Four Horsemen of Market Risk”.

Small Cap/Mega Cap Ratio

The level of the Russell 2000 Index – which tracks the performance of the 2000 smallest stocks by market cap in the Russell 3000 – divided by the level of the S&P 100 – which tracks the performance of the 100 largest stocks by market cap in the S&P 500. This intramarket analysis metric is valuable for two reasons: 1) because it often breaks out to a bullish VAMS state ahead of a Dominant Market Regime phase transition to GOLDILOCKS or REFLATION – the two “risk on” GRID Regimes in our framework; and 2) because it often breaks down to a bearish VAMS state ahead of a Dominant Market Regime phase transition to INFLATION or DEFLATION – the two “risk off” GRID Regimes in our framework. The Small Cap/Mega Cap Ratio is at the leading edge of our process for assessing and proactively positioning for changing market conditions and, thus, is one of our “Four Horsemen of Market Risk”.

Value/Growth Ratio

The level of the Russell 1000 Value Index – which tracks the performance of the 1000 largest US equities by market cap that also feature below-median price-to-book ratios and forecasted growth rates – divided by the level of the Russell 1000 Growth Index – which tracks the performance of the 1000 largest US equities by market cap that also feature above-median price-to-book ratios and forecasted growth rates. This intramarket analysis metric is valuable for two reasons: 1) because it often breaks out to a bullish VAMS state ahead of a Dominant Market Regime phase transition to GOLDILOCKS or REFLATION – the two “risk on” GRID Regimes in our framework; and 2) because it often breaks down to a bearish VAMS state ahead of a Dominant Market Regime phase transition to INFLATION or DEFLATION – the two “risk off” GRID Regimes in our framework. The Value/Growth Ratio is at the leading edge of our process for assessing and proactively positioning for changing market conditions and, thus, is one of our “Four Horsemen of Market Risk”.

Skew

The level spread between the Implied Volatility (“Ivol”) of 3mo put options that are 10% out of the money (“OTM”) and the Ivol of 3mo call options that are 10% OTM. Our process analyses this relationship on a normalized basis by displaying the latest value(s) as a Z-Score of the trailing one-year sample. This metric is valuable because it helps investors quantify and fade extremes in consensus positioning by flagging meaningful deviations in the market-implied medium-term outlook for a given exposure.

Volatility Risk Premia

The percentage spread between the Implied Volatility (“Ivol”) of put options and Realized Volatility (“Rvol”). Our process features the Ivol of 30-day at the money (“ATM”) put options and the mean of 10-day, 20-day, and 30-day Rvol. 30-day ATM put options tend to be the most liquid and most sought-after exposures for the purposes of delta hedging by many large financial institutions – including most of the multi-manager hedge funds (“Pod Shops”) that consistently dominate equity market turnover. We relate 30-day ATM put Ivol to the mean of 10-day, 20-day, and 30-day Rvol to bias the outliers in our sample towards exposures that have very recently corrected. This metric is valuable because it helps investors quantify and fade extremes in consensus positioning by flagging an outsized willingness – or lack thereof – to protect against near-term downside risk in a given exposure.

Dispersion

The spread between the performance of high-performing asset market exposures and their low-performing counterparts. Our dispersion analysis features trailing one-month Sharpe Ratios across 50 unique US equity sectors and style factors. We use Sharpe Ratios instead of raw performance figures because the volatility adjustment better aligns the analysis with the underlying fund flows into and out of the featured sectors and style factors. This analysis is valuable because changes to the composition of the kinds of sectors and style factors featured in the upper and lower quintiles – specifically whether they are primarily cyclical or defensive in nature – is often a leading indicator for phase transitions to a “risk on” Dominant Market Regime from a “risk off” state prior – and vice versa. In addition to being a leading indicator for our Top-Down Market Regime nowcasting process, there are three ancillary benefits for tracking equity market performance in this matter: 1) it gives us a general sense of what GRID Regime other investors implicitly or explicitly think we are in; 2) it affords us a view on what sectors and style factors multi-manager hedge funds (“Pod Shops”) are forcing their PMs into and out of – either implicitly through delta hedging activity or explicitly through capital reallocation; and 3) it affords us a near real-time view on what sectors and style factor exposures Quant funds are bidding up and selling down.

Speculative Net Length

The net sum of all long and short positions in the futures and options markets for a given exposure, as tracked by the weekly CFTC Commitment of Traders Report. That report is released Friday evening featuring data through the previous Tuesday’s close. Our process analyzes both the level of Speculative Net Length and changes therein on a normalized basis by displaying the latest value(s) as a Z-Score of the trailing three-year and one-year samples, respectively. These metrics are valuable because our backtests generally indicate extreme values in excess of [2σ] trend to signal either meaningful reversals or dramatic accelerations in the underlying price momentum of the given asset.

Probable Range

The primary tool we use to spot inflection points in the short-term momentum of a security or index. Our Probable Range process offers a thoughtful evolution upon Benoit Mandelbrot’s Rescaled Range framework by explicitly linking the calculus of the Upper and Lower Boundaries of the Probable Range to a given exposure’s VAMS state, whereby the range is asymmetrically skewed to the upside if the exposure is bullish VAMS and asymmetrically skewed to the downside if the exposure is bearish VAMS. This feature is especially valuable because it helps investors reorient their risk management approach – i.e. buying dips vs. selling rips – much faster than traditional trading ranges during inflections in the medium-term price momentum and volatility characteristics of the asset.

GRID Asset Market Backtests

The primary tool we use to guide our overweight and underweight biases both within and across asset classes. The 42 Macro GRID Asset Market Backtests map the historical performance of liquid asset markets on a monthly frequency relative to the realized Bottom-Up Macro Regime observations for the US and Global economies on a trailing 25-year basis via the following six metrics: 1) average annualized returns; 2) percent positive ratios; 3) realized volatility; 4) covariance with US equity beta as measured by the S&P 500; 5) number of observations; and 6) percentile of average annualized returns relative to the total population of equity index, fixed income index, interest rate, commodity, currency, and volatility market exposures featured in our backtests. This regime segmentation exercise is especially valuable because it allows investors to understand which GRID Regimes and other macroeconomic impulses – e.g. monetary tightening, fiscal easing, etc. – cause asset markets to behave in certain manners. That understanding is doubly valuable because it allows investors to anticipate changes in the behavior of a given exposure(s) or asset markets broadly based upon our anticipated evolution of the Dominant Market Regime. Those expectations are based upon our forecasted changes to the Bottom-Up Macro Regime for the US and Global economies and/or other macroeconomic impulses.

Blended Annualized Expected Return Ranking

One of the two primary tools we use to help investors visualize the potential reward of going long a given exposure(s) based upon our anticipated evolution of the Dominant Market Regime. Those expectations are based upon our forecasted changes to the Bottom-Up Macro Regime for the US and Global economies and/or other macroeconomic impulses. The 42 Macro GRID Asset Market Backtests segment the monthly annualized expected returns of liquid asset markets into the following seven categories: 1) the observed Bottom-Up Macro Regime for the US economy; 2) size of the observed growth delta on a -2σ to +2σ spectrum; 3) size of the observed inflation delta on a -2σ to +2σ spectrum; 4) observed changes to the Fed Funds Rate in 25bps increments; 5) observed changes to the Fed’s balance sheet in $25bn increments; 6) observed changes in the federal budget balance as a percentage of nominal GDP; and 7) the observed Bottom-Up Macro Regime for the World economy. Our Blended Annualized Expected Return Ranking combines each of these sub-backtests into a single annualized expected return figure that we rank in ascending order across each of the five liquid asset classes: 1) US Equities; 2) Global Equities; 3) Commodities; 4) Fixed Income; and 5) Foreign Exchange. These rankings are valuable because they feed directly into the Risk/Reward Scatter Plots that we use to help investors anticipate the relative performance of exposures within those asset classes.

Blended Percent Positive Ratio Ranking

One of the two primary tools we use to help investors visualize the potential reward of going long a given exposure(s) based upon our anticipated evolution of the Dominant Market Regime. Those expectations are based upon our forecasted changes to the Bottom-Up Macro Regime for the US and Global economies and/or other macroeconomic impulses. The 42 Macro GRID Asset Market Backtests segment the percent positive ratios of liquid asset markets into the following seven categories: 1) the observed Bottom-Up Macro Regime for the US economy; 2) size of the observed growth delta on a -2σ to +2σ spectrum; 3) size of the observed inflation delta on a -2σ to +2σ spectrum; 4) observed changes to the Fed Funds Rate in 25bps increments; 5) observed changes to the Fed’s balance sheet in $25bn increments; 6) observed changes in the federal budget balance as a percentage of nominal GDP; and 7) the observed Bottom-Up Macro Regime for the World economy. Our Blended Percent Positive Ratio Ranking combines each of these sub-backtests into a single percent positive ratio figure that we rank in ascending order across each of the five liquid asset classes: 1) US Equities; 2) Global Equities; 3) Commodities; 4) Fixed Income; and 5) Foreign Exchange. These rankings are valuable because they feed directly into the Risk/Reward Scatter Plots that we use to help investors anticipate the relative performance of exposures within those asset classes.

Blended Volatility Ranking

One of the two primary tools we use to help investors visualize the potential risk of going long a given exposure(s) based upon our anticipated evolution of the Dominant Market Regime. Those expectations are based upon our forecasted changes to the Bottom-Up Macro Regime for the US and Global economies and/or other macroeconomic impulses. The 42 Macro GRID Asset Market Backtests segment the realized volatility of liquid asset markets into the following seven categories: 1) the observed Bottom-Up Macro Regime for the US economy; 2) size of the observed growth delta on a -2σ to +2σ spectrum; 3) size of the observed inflation delta on a -2σ to +2σ spectrum; 4) observed changes to the Fed Funds Rate in 25bps increments; 5) observed changes to the Fed’s balance sheet in $25bn increments; 6) observed changes in the federal budget balance as a percentage of nominal GDP; and 7) the observed Bottom-Up Macro Regime for the World economy. Our Blended Volatility Ranking combines each of these sub-backtests into a single volatility figure that we rank in ascending order across each of the five liquid asset classes: 1) US Equities; 2) Global Equities; 3) Commodities; 4) Fixed Income; and 5) Foreign Exchange. These rankings are valuable because they feed directly into the Risk/Reward Scatter Plots that we use to help investors anticipate the relative performance of exposures within those asset classes.

Blended Covariance Ranking

One of the two primary tools we use to help investors visualize the potential risk of going long a given exposure(s) based upon our anticipated evolution of the Dominant Market Regime. Those expectations are based upon our forecasted changes to the Bottom-Up Macro Regime for the US and Global economies and/or other macroeconomic impulses. The 42 Macro GRID Asset Market Backtests segment the covariance of liquid asset markets into the following seven categories: 1) the observed Bottom-Up Macro Regime for the US economy; 2) size of the observed growth delta on a -2σ to +2σ spectrum; 3) size of the observed inflation delta on a -2σ to +2σ spectrum; 4) observed changes to the Fed Funds Rate in 25bps increments; 5) observed changes to the Fed’s balance sheet in $25bn increments; 6) observed changes in the federal budget balance as a percentage of nominal GDP; and 7) the observed Bottom-Up Macro Regime for the World economy. Our Blended Covariance Ranking combines each of these sub-backtests into a single covariance figure that we rank in ascending order across each of the five liquid asset classes: 1) US Equities; 2) Global Equities; 3) Commodities; 4) Fixed Income; and 5) Foreign Exchange. These rankings are valuable because they feed directly into the Risk/Reward Scatter Plots that we use to help investors anticipate the relative performance of exposures within those asset classes.

Risk/Reward Scatter Plots

The primary tools we use to help investors visualize the expected relative performance of asset market exposures according to our GRID Asset Market Backtests. Our analysis features the mean Blended Annualized Expected Return Ranking and Blended Percent Positive Ratio Ranking on the y-axis and the mean Blended Volatility Ranking and Blended Covariance Ranking on the x-axis. These charts are valuable because they help investors more deeply understand the expected performance of going long or short a given exposure(s) than traditional regime segmentation backtests, which tend to narrowly focus on reward statistics and/or the primary regime.