In Search of the Perfect Recession Indicator

The downturn in the energy sector and persistent economic weakness abroad has caused the investment community to become increasingly focused on the possibility of a U.S. recession.  In this piece, I’m going to examine a historically powerful indicator that would seem to rule out that possibility, at least for now.

The following chart (source: FRED) shows the seasonally-adjusted U.S. civilian unemployment rate (UE) from  January 1948 to January 2016:


As the chart illustrates, the unemployment rate is a lagging indicator of recession.  By the time high unemployment takes hold in an economy, a recession has usually already begun.

In contrast with the absolute level, the trend in the unemployment rate–the direction that the rate is moving in–is a coincident indicator of recession, and can sometimes even be a leading indicator.  As the table below shows, in each of the eleven recessions that occurred since 1948, the trend in the unemployment rate turned higher months before the recession began.  The average lead for the period was 3.45 months.


Admittedly, the phrase “turning higher” is ambiguous.  We need to be more precise, and so we’re going to define the phrase in terms of trailing moving averages.  That is, we’re going to say that the unemployment rate trend has turned higher whenever its current value crosses above the moving average of its trailing values over some period, and that the unemployment rate trend has turned lower whenever its current value falls below the average of its trailing values over some period.

In the following chart, we plot the unemployment rate alongside its trailing 12 month moving average from January 1948 to January 2016.  The red and green circles delineate important crossover points, with red crossovers delineating upward (bearish) turns, and green crossovers delineating downward (bullish) turns:


As you can see, historically, whenever the unemployment rate has crossed above the moving average, a recession has almost always followed shortly thereafter.  Similarly, for every recession that actually did occur in the period, the unemployment rate successfully foreshadowed the recession in advance by crossing above its moving average.

The following chart takes the indicator back farther, from April 1929 to April 1947:


In contrast with the earlier chart, the indicator here appears to be a bit late. After capturing the onset of the Great Depression almost perfectly, the indicator misses the onset of the 1937 and 1945 recessions by a few months.   It’s not alone in that respect–the 1937 and 1945 recessions were missed by pretty much every other recession indicator on the books.

The Fed is well aware of the recession forecasting power of the trend in the unemployment rate.  New York Fed president William Dudley discussed the matter explicitly in a speech just last month:

“Looking at the post-war period, whenever the unemployment rate has increased by more than 0.3 to 0.4 percentage points, the economy has always ended up in a full-blown recession with the unemployment rate rising by at least 1.9 percentage points. This is an outcome to avoid, especially given that in an economic downturn the last to be hired are often the first to be fired. The goal is the maximum sustainable level of employment—in other words, the most job opportunities for the most people over the long run.”

As far as the U.S. economy is concerned, the indicator’s current verdict is clear: no recession.  We may enter a recession later this year, or next year, but we’re not in a recession right now.

Individual energy-exposed regions of the country, however, are in recession, and the indicator is successfully flagging that fact.  The following chart shows the unemployment rate for Houston (source: FRED):


Per the indicator, Houston’s economy is solidly in recession.  We know the reason why: the plunge in oil prices.

Dallas is tilting in a similar direction.  But it’s a more diversified economy, with less exposure to oil and gas production, so the tilt isn’t as strong (source: FRED):


If the larger U.S. economy is not in a recession, what is the investing takeaway?  The takeaway is that we should be constructive on risk, with a bias towards being long equities, given the reduced odds of a large market drop.  Granted, recessions aren’t the only drivers of large market drops, but they’re one of the few drivers that give clear signs of their presence before the drops happen, so that investors can get out of the way. Where they can be ruled out, the risk-reward proposition of being long equities improves dramatically.

Now, the rest of this piece will be devoted to an rigorous analysis of the unemployment rate trend as a market timing indicator.  The analysis probably won’t make sense to those readers that haven’t yet read the prior piece on “Growth-Trend Timing”, so I would encourage them to stop here and go give it a skim.  What I say going forward will make more sense.

To begin, recall that GTT seeks to improve on the performance of a conventional trend-following market timing strategy by turning off the trend-following component of the strategy (i.e., going 100% long no matter what) during periods where the probability of recession is low.  In this way, GTT avoids substantial whipsaw losses, while incurring only a slightly increased downside risk.

Using the unemployment rate as an input, the specific trading rule for GTT would be:

(1) If the unemployment rate trend is downward, i.e., not indicating an oncoming recession, then go 100% long U.S. equities.

(2) If the unemployment rate trend is upward, indicating an oncoming recession, then defer to the price trend.  If the price trend is upward, then go 100% long U.S. equities.  If the price trend is downward, then go to cash.

To summarize, GTT will be 100% invested in the market unless the unemployment rate trend is upward at the same time that the price trend is downward.  Together, these indicators represent a double confirmation of danger that forces the strategy to take a safe position.

The following chart shows the strategy’s performance in U.S. equities from January 1930 to January 2016.  The unemployment rate trend is measured in terms of the position of the unemployment rate relative to its trailing 12 month moving average, where above signifies an upward trend, and below signifies a downward trend.  The price trend is measured in a similar way, based on the position of the market’s total return index relative to the trailing 10 month moving average of that index:


The blue line is the performance of the strategy, GTT.  The green line is the performance of a pure and simple moving average strategy, without GTT’s recession filter.  The dotted red line is the outperformance of GTT over the simple moving average strategy. The yellow line is a rolled portfolio of three month treasury bills. The gray line is buy and hold.  The black line is GTT’s “X/Y” portfolio–i.e., a portfolio with the same net equity and cash exposures as GTT, but achieved through a constant allocation over time, rather than through in-and-out timing moves (see the two prior pieces for a more complete definition).  The purple bars indicate periods where the unemployment rate trend is downward, ruling out recession.  During those periods, the moving average strategy embedded in GTT gets turned off, directing the strategy to take a long position no matter what.

As the chart illustrates, the strategy beats buy and hold (gray) as well as a simple moving average (green) strategy by over 150 basis points per year.  That’s enough to triple returns over the 87 year period, without losing any of the moving average strategy’s downside protection.

In the previous piece, we looked at the following six inputs to GTT:

  • Real Retail Sales Growth (yoy, RRSG)
  • Industrial Production Growth (yoy, IPG)
  • Real S&P 500 EPS Growth (yoy, TREPSG), modeled on a total return basis.
  • Employment Growth (yoy, JOBG)
  • Real Personal Income Growth (yoy, RPIG)
  • Housing Start Growth (yoy, HSG)

We can add a seventh input to the group: the unemployment rate trend (UE vs. 12 MMA). The following table shows GTT’s excess performance over a simple moving average strategy on each of the seven inputs, taken individually:


As the table shows, the unemployment rate trend beats all other inputs. To understand why it performs better, we need to more closely examine what GTT is trying to accomplish.

Recall that the large market downturns that drive the outperformance of trend-following strategies tend to happen in conjunction with recessions.  When a trend-following strategy makes a switch that is not associated with an ongoing or impending recession, it tends to incur whipsaw losses.  (Note: these losses were explained in thorough detail in the prior piece).

What GTT tries to do is use macroeconomic data to distinguish periods where a recession is likely from periods where a recession is unlikely.  In periods where a recession is unlikely, the strategy turns off its trend-following component, taking a long position in the market no matter what the price trend happens to be.  It’s then able to capture the large downturns that make trend-following strategies profitable, without incurring the frequent whipsaw losses that would otherwise detract from returns.

The ideal economic indicator to use in the strategy is one that fully covers the recessionary period, on both sides.  The following chart illustrates using the 2008 recession as an example:


We want the red area, where the recession signal is in and where the trend-following component is turned on, to fully cover the recessionary period, from both ends. If the signal comes in early, before the recession begins, or goes out late, after the recession has ended, the returns will not usually be negatively impacted.  The trend-following component of the strategy will take over during the period, and will ensure that the strategy profitably trades around the ensuing market moves.

What we categorically don’t want, however, is a situation where the red area fails to fully cover the recessionary period–in particular, a situation where the indicator is late to identify the recession.  If that happens, the strategy will not be able to exit the market on the declining trend, and will risk of getting caught in the ensuing market downturn.  The following chart illustrates the problem using the 1937 recession as an example:


As you can see, the indicator flags the recession many months after it has already begun. The trend-following component therefore doesn’t get turned on until almost halfway through the recessionary period.  The risk is that during the preceding period–labeled the “danger zone”–the market will end up suffering a large downturn.  The strategy will then be stuck in a long position, unable to respond to the downward trend and avoid the losses.  Unfortunately for the strategy, that’s exactly what happened in the 1937 case.  The market took a deep dive in the early months of the recession, before the indicator was flagging.  The strategy was therefore locked into a long position, and suffered a large drawdown that a simple unfiltered trend-following strategy would have largely avoided.

We can frame the point more precisely in terms of two concepts often employed in the area of medical statistics: sensitivity and specificity.  These concepts are poorly-named and very easy to confuse with each other, so I’m going to carefully define them.

The sensitivity and specificity of an indicator are defined as follows:

  • Sensitivity: the percentage of actual positives that the indicator identifies as positive.
  • Specificity: the percentage of actual negatives that the indicator identifies as negative.

To use an example, suppose that there are 100 recessionary months in a given data set.  In 86 of those months, a recessionary indicator comes back positive, correctly indicating the recession.  The indicator’s sensitivity to recession would then be 86 / 100 = 86%.

Alternatively, suppose that there are 700 non-recessionary months in a given data set.  In 400 of those non-recessionary months, a recessionary indicator comes back negative, correctly indicating no recession. The indicator’s specificity to recession would then be 400 / 700 = 57%.

More than anything else, what GTT needs is an indicator with a high sensitivity to recession–an indicator that rarely gives false negatives, and that will correctly indicate that a recession is happening whenever a recession is, in fact, happening.

Having a high specificity to recession, in contrast, isn’t as important to the strategy, because the strategy has the second layer of the price trend to protect it from unnecessary switches.   If the indicator sometimes overshoots with false positives, indicating a recession when there is none, the strategy won’t necessarily suffer, because if there’s no recession, then the price trend will likely be healthy.  The healthy price trend will keep the strategy from incorrectly exiting the market on the indicator’s mistake.

Of all the indicators in the group, the unemployment rate trend delivers the strongest performance for GTT because it has the highest recession sensitivity.  If there’s a recession going on, it will almost always tell us–better than any other single recession indicator.  In situations where no recession is happening, it may give false positives, but that’s not a problem, because unless the false positives coincide with a downward trend in the market price–an unlikely coincidence–then the strategy will stay long, avoiding the implied whipsaw.

For comparison, the following tables show the sensitivity and specificity of the different indicators across different time periods:




As the tables confirm, the unemployment rate  has a very strong recession sensitivity, much stronger than any other indicator.  That’s why it produces the strongest performance.

Now, we can still get good results from indicators that have weaker sensitivities.  We just have to aggregate them together, treating a positive indication from any of them as a positive indicatation for the aggregate signal.  That’s what we did in the previous piece. We put real retail sales growth and industrial production growth together, housing start growth and real personal income growth together, and so on, increasing the sensitivity of the aggregate signal at the expense of its specificity.

Right now, only two of the seven indicators are flagging recession: industrial production growth and total return EPS growth.  We know why those indicators are flagging recession–they’re getting a close-up view of the slowdown in the domestic energy sector and the unrelated slowdown in the larger global economy.  Will they prove to be right?  In my view, no.  Energy production is a small part of the US economy, even when multipliers are considered.  Similarly, the US economy has a relatively low exposure to the global economy, even though a significant portion of the companies in the S&P 500 are levered to it.

Even if we decide to go with industrial production growth (or one of its ISM siblings) as the preferred indicator, recent trends in that indicator are making the recession call look shakier.  In the most recent data point, the indicator’s growth rate has turned up, which is not what we would expect to be seeing right now if the indicator were right and the other indicators were wrong:


Now, the fact that a U.S. recession is unlikely doesn’t mean that the market is any kind of buying opportunity.  Valuation can hold a market back on the upside, and the market’s current valuation is quite unattractive.  At a price of 1917, the S&P 500’s trailing operating P/E ratio is 18.7.  Its trailing GAAP P/E ratio is 21.5.  Those numbers are being achieved on peaking profit margins–leaving two faultlines for the market to crack on, rather than just one.  Using non-cyclical valuation measures, which reflect both of those vulnerabilities, the numbers get worse.

My view is that as time passes, the market will continue to acclimatize to the two issues that it’s been most worried about over the last year: (1) economic weakness and potential instability in China and (2) the credit implications of the energy downturn.  A similar acclimatization happened with the Euro crisis.  It always seems to happen with these types of issues.  The process works like this.  New “problems” emerge, catching investors off-guard.  Many investors come to believe that this is it, the start of the “big” move lower. The market undergoes a series of gyrations as it wrestles with the problems. Eventually, market participants get used to them, accustomed to their presence, like a swimmer might get accustomed to cold water.  The sensitivity, fear and reactivity gradually dissipate. Unless the problems continue to deteriorate, investors gravitate back into the market, even as the problems are left “unsolved.”

Right now, there’s a consensus that an eventual devaluation of the yuan, with its attendant macroeconomic implications, is itself a “really bad thing”, or at least a consequence of a “really bad thing” that, if it should come to pass, will produce a large selloff in U.S. equities.  But there’s nothing privileged or compelling about that consensus, no reason why it should be expected to remain “the” consensus over time.  If we keep worrying about devaluations, and we don’t get them, or we do get them, and nothing bad happens, we will eventually grow less concerned about the prospect, and will get pulled back into the market as it grinds higher without us.  In actuality, that seems to be what’s already happening.

Valuation-conscious investors that are skeptical of the market’s potential to deliver much in the way of long-term returns–and I would include myself in that category–do have other options.  As I discussed in a piece from last September, we can take advantage of elevated levels of volatility and sell puts or covered calls on a broad index such as the S&P 500 or the Russell 2000.  By foregoing an upside that we do not believe to be attractive to begin with, we can significantly pad our losses in a potential downturn, while earning a decent return if the market goes nowhere or up (the more likely scenario, in my view).

To check in on the specific trade that I proposed, on September 6th, 2015, with $SPY at 192.59, the bid on the 165 September 2016 $SPY put was 8.79.   Today, $SPY is at essentially the same price, but the value of the put has decayed substantially.  The ask is now 4.92.  On a mark-to-market basis, an investor that put a $1,000,000 into the trade earned roughly $4 per share–$24,000, or roughly 6% annualized, 6% better than the market, which produced nothing.

For the covered call version of the trade, the bid on the 165 September 2016 call was 33.55.  As of Friday, the ask is now 30.36.  On a mark-to-market basis, then, the investor has earned roughly $3.20 in the trade.  The investor also pocketed two $SPY dividends, worth roughly $2.23.   In total, that’s $5.43 per share, or roughly 8% annualized.  If the market continues to churn around 1900, the investor will likely avoid assignment and get to stay in the trade, if not through both of the upcoming dividends, then at least through the one to be paid in March.

To summarize, right now, the data is telling us that a large, recessionary downturn is unlikely.  So we want to be long.  At the same time, the heightened state of valuations and the increasing age of the current cycle suggest that strong returns from here are unlikely.   In that kind of environment, it’s attractive to sell downside volatility.  Of course, in selling downside volatility, we lose the ability to capitalize on short-term trading opportunities. Instead of selling puts last September, for example, we could have bought the market, sold it at the end of the year at the highs, and then bought it back now, ready to repeat again. But that’s a difficult game to play, and an even more difficult game to win at.  For most of us, a better approach is to identify the levels that we want to own the market at, and get paid to wait for them.

While we’re on the topic of GTT and recession-timing, I want to address a concern that a number of readers have expressed about GTT’s backtests.  That concern pertains to the impact of data revisions.  GTT may work well with the revised macroeconomic data contained in FRED, but real-time investors don’t have access to that data–all they have access to is unrevised data.  But does the strategy work on unrevised data?

Fortunately, it’s possible (though cumbersome) to access unrevised data through FRED. Starting with the unemployment rate, the following chart shows the last-issue revised unemployment rate alongside the first-issue unrevised unemployment rate from March 1961 to present:


As you can see, there’s essentially no difference between the two rates.  They overlap almost perfectly, confirming that the revisions are insignificant.

Generally, in GTT, the impact of revisions is reduced by the fact that the strategy pivots off of trends and year-over-year growth rates, rather than absolute levels and monthly growth rates, where small changes would tend to have a larger effect.

The following chart shows the performance of GTT using from March 1961 to present using first-issue unrevised unemployment rate data (orange) and last-issue revised unemployment rate data (blue).  Note that unrevised data prior to March 1961 is not available, which is why I’ve chosen that date as the starting point:


Interestingly, in the given data set, the strategy actually works better on unrevised data. Of course, that’s likely to be a random occurrence driven by luck, as there’s no reason for unrevised data to produce a superior performance.

The following chart shows the performance of GTT using available unrevised and revised data for industrial production growth back to 1928:

IPG rvur

In this case, the strategy does better under the revised data, even though both versions outperform the market and a simple moving average strategy.  The difference in performance is worth about 40 basis points annually, which is admittedly significant.

One driver of the difference between the unrevised and revised performance for the industrial production case is the fact that the unrevised data produced a big miss in late 2011, wrongly going negative and indicating recession when the economy was fine.  Recently, a number of bearish commentators have cited the accuracy of the industrial production growth indicator as a reason for caution, pointing out that the indicator that has never produced sustained negative year over year growth outside of recession.  That may be true for revised data, but it isn’t true for unrevised data, which is all we have to go on right now.  Industrial production growth wrongly called a recession in 2011, only to get revised upwards several months later.

The following chart shows the performance of GTT using available unrevised and revised data for real retail sales growth:

RRSG rvur

The unrevised version underperforms by roughly 20 basis points annually.

The following chart shows the performance of GTT using available unrevised and revised data for job growth:

JOBG rvur

For job growth, the two versions perform about the same.

Combining indicators shrinks the impact of inaccuracies, and reduces the difference between the unrevised and revised cases.  The following chart illustrates, combining industrial production and job growth into a single “1 out of 2” indicator:


Unfortunately, unrevised data is unavailable for EPS (the revisions would address changes to SEC 10-Ks and 10-Qs), real personal income growth, and housing start growth.  But the tests should provide enough evidence to allay the concerns.  The first-issue data, though likely to be revised in small ways, captures the gist of what is happening in the economy, and can be trusted in market timing models.

In a future piece, I’m going to examine GTT’s performance in local currency foreign equities.  GTT easily passes out-of-sample testing in credit securities, different sectors and industries, different index constructions (where, for example, the checking days of the month are chosen randomly), and individual securities (which simple unfiltered trend-following strategies do not work in).  However, the results in foreign securities are mixed.

If we use U.S. economic data as a filter to time foreign securities, the performance turns out to be excellent.  But if we use economic data from the foreign countries themselves, then the strategy ends up underperforming a simple unfiltered trend-following strategy.  Among other things, this tells us something that we could probably have already deduced from observation: the health of our economy and our equity markets is more relevant to the performance of foreign equity markets than the health of their own economies.  This is especially true with respect to large downward moves–the well-known global “crises” that drag all markets down in unison, and that make trend-following a historically profitable strategy.

This entry was posted in Uncategorized. Bookmark the permalink.