Movement’s macro model seeks to reduce stock exposure when there’s a high probability of a recession.
The core components of the macro model are:
- Monitoring macroeconomic indicators to assess the probability of a recession.
- Adjusting stock exposure based on this probability.
- Rebalancing the model to reflect recent economic data.
The macro model monitors three economic indicators: retail sales, industrial production, and the unemployment rate. These indicators cover three broad aspects of the economy: consumer spending, business activity, and the labor market. Each indicator has 70+ years of data.
Retail sales measure the dollar value of merchandise sold by retailers. Increasing retail sales indicates strong consumer spending and a strengthening economy. Historically, recessions have typically occurred when retail sales have fallen over the past year. This is shown when the blue line falls below 0%. Recessions are highlighted in grey.
Industrial production measures the raw volume of goods produced by manufacturing companies. Increasing industrial production is associated with strong business activity for industrial companies. Historically, recessions have typically occurred when industrial production has fallen over the past year. This is shown when the yellow line falls below 0%.
The unemployment rate measures the percentage of the labor force that is unemployed. A decreasing unemployment rate indicates a stronger job market. Historically, recessions have typically occurred when the unemployment rate has significantly risen.
The macro model specifies a filter level for each indicator that’s historically been associated with a higher probability of recession. For both retail sales and industrial production, that level is when an indicator’s annual change is less than 0%. For example, if the annual change in retail sales is -2%, then retail sales have fallen over the past year. That would mean that consumers are spending less money and that part of the economy is weaker.
Since the unemployment rate is a percentage rather than an annual change, its filter level is reached if the unemployment rate is higher than its 12 month average. For example, suppose the average unemployment rate over the past 12 months was 5.0%. If the current unemployment rate is 7.0%, the unemployment rate is rising and above its recent average. This means that more people are out of work and the labor market is deteriorating.
The table below shows each recession since 1950 as well as whether an economic indicator was below its filter level. The drawdown column shows the max peak-to-trough price loss of the S&P 500 during each recession. The average drawdown is -21.7%. The goal of the macro model is to avoid some of this drawdown by reducing stock exposure during recessions.
The table below shows how frequently each economic indicator has been below its filter level, both during and outside of recessions. For example, the annual change in retail sales has fallen below zero 70% of the time during recessions since 1950. This has only happened 15% of the time outside of recessions.
The right most column shows the amount of time that at least two of the indicators have been below their filter levels. This is the main input for the macro model: If two of the three economic indicators are below their filter levels, there’s a higher probability of recession, and the macro model reduces risk and rotates from stocks to bonds.
It’s helpful to look at one recession in isolation to see how the indicators influenced the model’s positioning.
The table below shows each indicator during the most recent recession. When the recession started in December 2007, only the unemployment rate was below its filter level. A reading of -0.2% means that the unemployment rate in December 2007 was 0.2% higher than its 12 month average, meaning that the labor market was weakening.
Both retail sales and industrial production were healthy in December 2007, with each pointing to increased growth over the prior year. This changed in February 2008, when retail sales fell and the indicator was below its filter level of 0%. Basically, consumers were starting to spend less money. In February 2008, two of the three indicators were below their filter levels. The macro model rotated from stocks to bonds.
The macro model’s baseline stock allocation is a 50/50 split between the same U.S. and international stock index funds used in the trend and passive models. Specifically, the models use VTI for U.S. stocks and VXUS for international stocks. Both are low-cost Vanguard total market funds. If there is a low probability of recession (one or zero indicators below their filter levels), the macro model is equally invested in both funds. If there is a higher probability of recession (two or more indicators below their filter levels), the macro model is invested in bonds.
The bond fund depends on an investor’s account type. If the account is tax deferred (like an IRA), the fund used is VGIT, an intermediate-term U.S. Treasury fund. If the account is taxable (like a brokerage account), the fund used is VTEB, an intermediate-term municipal bond fund.
Movement uses these two funds, rather than a blended fund that tracks the popular Bloomberg Barclays Aggregate Bond index, to avoid taking credit risk when the macro model avoids stock exposure. Movement also uses intermediate-term funds (rather than short-term or long-term bond funds) to avoid taking an active stance on the future direction of interest rates. This is explained further in the passive model page.
The chart below shows the macro model’s historical positioning:
The final step in the macro model is to rebalance the model to reflect recent data. Most active investors run monthly strategies based on data at the end of the month. Movement uses a different approach for two reasons: 1) monthly strategies implicitly bet on the last day of the month to be the best day to rebalance, and 2) monthly strategies tend to underestimate volatility.
The first reason is called timing luck and represents the amount of potential over or underperformance of a model solely due to its rebalancing date.1 The second reason why Movement doesn’t rebalance monthly is because monthly observations don’t capture volatility that happens within a month. For example, the max drawdown of international stocks in October 2008 based on monthly data is -22%. The same metric when using daily data is -34%. Monthly models observe fewer data points and underestimate risk.
Movement’s solution is to rebalance the macro model once per week but freeze the model’s stance for thirty days after a new position is initiated. This helps avoid wash sales in taxable accounts and also reduces back-and-forth trading.
There’s one other aspect of the macro model. Economic data isn’t known in real time. For example, you can’t use February retail sales data in February since the data wasn’t released yet. Each economic indicator must be lagged so that a model is only using data that was available in the past. Movement’s macro model incorporates this lag for each indicator.
The macro model description is now complete. It monitors economic data that has historically indicated a higher probability of recession, adjusts stock exposure based on this probability, and then rebalances when the model’s positioning changes.