Reflections [Expanded version]
Yield Curve Inversion Forecast Update Nov 2018
Based on the methodology discussed here we hereby update our U.S Yield-curve inversion forecast and subsequent recession and stock market peak forecasts. All the forecast dates have moved further back by 5 months:
2nd derivative of WLEI posts warning
The RecessionALERT Weekly Leading Economic Index (WLEI) is essentially a first derivative indicator (rate of change). We can create the second derivative by measuring the percentage of time the WLEI has historically spent above the current reading. The history shows us that this is a good leading indicator for another WLEI metric, namely the percentage of WLEI components in recession territory (we call this the “WLEI Diffusion”):
The 2nd derivative is diverging from the WLEI Diffusion, and as we mentioned, in the past when this has occurred, the Diffusion eventually is forced to play catch-up.
This implies more structural damage could be ahead for the WLEI, that is not just limited to a deterioration in its levels.
The components of the WLEI currently in recession are all related to the corporate bond market (AAA/BAA etc.) The Credit spreads market composite and the labour market composite are looking impervious at this stage. This leaves any further damage to the Diffusion likely to originate from the stock market or the Treasury/Corporate bond spreads composite.
Another useful leading indicator for the WLEI is ECRI’s own WLI, which confirms that more damage ahead for the WLEI is likely:
The ECRI WLI can lead the WLEI on many occasions, as it is composed of some longer leading and/or more sensitive indicators than the WLEI.
When we constructed the WLEI our focus was less on longer leading characteristics and more on less “false positives”. As you can see from the chart the WLI has had 4 false positives so far this business cycle versus 1 for the WLEI. It’s a good pairing for high frequency leading data – you take the ECRI WLI as the 1st warning and the WLEI as second. If both are flagging recession, which looks increasingly likely in the next few weeks, you obviously need to take note and start consulting the more robust monthly models or the SuperIndex which is composed of many monthly models.
SP500 was ahead of itself but tailwinds could be back
During the 6 months running from March to September 2018, the SP500 was running counter to the seasonal average returns profile of the 4-year U.S Presidential Cycle:
It appears the seasonality averages eventually got their way and the SP500 fell hard in October 2018, in what was supposed to be a strong month. In all likelyhood the tailwinds of one of the strongest periods in the Presidential Cycle will come to bear and we should expect good returns for the next 8-10 months.
Below is a slightly outdated (as of 2016), but no less relevant, statistical analysis of the Presidential Cycle. We have just entered the 2nd month of the “PowerZone” which is the 11th month of the Mid-term year.
The height of the bars represent the average gain for the month, and the percentages within the bars represent how many of the months posted gains, followed by the gain/loss ratio. So for November 2018, the 2nd month of the “PowerZone”, we expect an average return of 2.5% with a 78% probability of a positive gain this month. The gains from the winning months in the 2nd month of the “PowerZone” outpace the losses 4.6 times to 1.
January 2019 promises to be the most powerful month of the entire 4 year cycle, with greater than 4% gains and a heady 93% winning percentage. The model suggests a dose of leverage between now and August 2019, although I prefer to scale back any leverage beyond April 2019.
The historical returns from the above suggested strategy of buying on 2X leverage from the 10th Month of the Mid-term year, selling in the 8th month of the pre-election year, and buying again with no leverage in the final month of the pre-election year and then selling again at points “SELL-A” (best winning ratio), “SELL-B” (best return) and “SELL-C” (most in the market) are simply astounding. In fact, I am not even going to bother putting them up for display lest your glasses become too rose-tinted.
Yield curve inversion & recession forecast
There is naturally a lot of focus on the U.S yield-curve at the moment, as it moves relentlessly toward inversion (when short-term rates are higher than long-term rates.) Can the history of the yield-curve inversion provide for useful forecasting as to the start dates of the next U.S recession?
The 10’s vs. 1’s yield-curve and U.S recessions in the post-war era are displayed below, where it is clear that the nine recessions since 1956 were predicted by yield-curve inversion, with one false positive in 1966.:
The chart below shows how many months the yield-curve inverted before each of the recessions. We ignored the false positive in 1966 to give the yield-curve the benefit of the doubt. The smallest lead-times to recession average 8 months, the median lead-time is 12 months and the longest lead-times average 20 months:
So all that is required now is to project the current yield-curve trajectory into the future to see when it will invert, arriving at a date of February 2019:
From this projected inversion date we can make some estimates on when we are most likely to see the arrival of a U.S recession based on the average short lead-time, the median lead-time and the average longest lead-time to a recession:
Now it is accepted that the U.S stock market is a forward-looking indicator and will peak before the onset of a recession. Sure enough, in our 2012 research note: “Recession: Just How Much Warning Is Useful Anyway?” we showed that any generalized stock market defensive actions 5 months before the onset of recession are likely to prove unproductive, based on the last 5 recessions. This would imply we need to start preparing for a U.S stock market peak around the following dates:
There is a lot of commentary around the yield-curve no longer being an effective recession warning indicator due to the artificial low-interest rate environment created by the FED. There is also the timeless stock market quip that when you start hearing that “this time is different” that things will turn out anything but different. There are a lot of theories as to why an inverted yield-curve for even short periods will guarantee a recession, but the one that made the most sense to me follows:
“Banks make longer-duration loans to clients who pay the longer term rates. These loans are the assets of the bank. Depositors lend money to the bank at the short-term interest rate. These are the bank’s liabilities. When the bank pays a higher rate on its liabilities than what it earns on its assets, it loses the incentive to forward more loans to businesses and stops lending. This causes a “credit crunch” or the falling availability of credit. Businesses struggle to roll over their current account credit and they are forced to downsize and lay off workers, and we enter a recession. The moment the Fed engineers short-term interest rates to go below long-term interest rates, the banks can generate a profit again, credit expansion will resume and the stock market and economy can recover.”
If one assumes the above narrative to be true, then it doesn’t matter how low the interest rate environment currently engineered by the FED is – when short-term rates are higher than long-term rates a credit crunch, stock market peak, and recession is inevitable.
We can make some interesting observations by measuring the area under the yield-curve when it is above and below zero respectively as depicted below:
We note that it does not take a lot (as measured by cumulative inversion area below zero) to trigger a credit crunch and recession. We can also deem the area above zero as representing “excess liquidity” and quite clearly the area of the current credit-easing cycle dwarfs all those preceding it. Many believe that the larger the credit easing excess, the worse the subsequent recession, but this is untested by any research we have conducted or come across yet.
World headed for cyclical slowdown
Despite the U.S leading economic indicators appearing healthy, the global economy appears to be headed for a slow down, with only 34% of the 40 countries we track having leading economic indicators (LEI’s) signalling growth ahead, and the actual GDP-weighted Global LEI growth now below zero:
The specific country details are displayed below:
The European countries, representing some 25% of world economic output have taken a decidedly worrisome turn :
Many of these LEI’s include sentiment data, and its probably a fair assumption to assume that the “trade wars” talk doing the rounds of late have a big part to play in these negative future growth projections.
Whilst the RecessionALERT U.S Leading index is currently looking robust, we cannot ignore the fact that there is a not-insignificant 40% correlation between the movements of the U.S LEI and the Global one. In fact a visual inspection shows that downturns in the Global LEI invariably always lead to downturns in the US LEI:
This correlation by no means implies a US recession, but it undoubtedly is likely to put downward pressure on the U.S LEI in the coming months.
It is early days for the co-incident data and no significant signs of a slowdown can yet be witnessed among them. To this end, here is an interesting chart of country GDP growth from 1Q2017 to 1Q2018:
If you are a RecessionALERT subscriber, you can view the comprehensive global report for May 2018 from the REPORTS menu. You can subscribe to RecessionALERT for a nominal fee over here.
Like this kind of information? We post occasionally to our public Twitter feed here : https://twitter.com/RecessionAlert2
Dealing with a runaway market
Those of you who have been following us since 2010 will identify us a perma-bulls. Even in the depths of the ECRI 2012 /Hussman recession calls we were firmly bullish on the US economy and stock market – quite contrary to the popular consensus at the time. Those subscribers who have been diligently following the RAVI SP500 forecasting model and its consistently accurate bullish forecasts will have noticed this year that all the targets we have set for 3Q2018 have been met on our Dashboard:
This means, for the first time since we have been running this model, that RAVI SP500 valuations have finally run ahead of themselves. This is not to say this exuberance will not continue for some time, but it is a warning for those who like to deploy valuation risk metrics in their asset allocation models.
Now for a long time, various valuation models have been at elevated levels. Here are a few below:
Despite these elevated levels, RAVI was forecasting bullish returns for 2016 and 2017. All these models above have very good correlations to 10-14 year ahead SP500 returns and in many instances of late were actually forecasting muted to negative 10-year ahead returns. But just because a valuation model is forecasting a negative 10-year return doesn’t mean that 1-3 year returns will be poor! This mistake is consistently made by forecasters! A case in point is shown below, using Warren Buffets famous valuation metric. Since 2014 this model has been persistently forecasting low to negative 10-year ahead returns – but that didn’t stop the SP500 roaring ahead!
Does this mean Warren Buffets indicator is useless? Of course not – it means it’s been interpreted incorrectly! In all likelihood, in 10 years’ time we come back to this chart and remark on how accurate it was since the black line (actual 10-yr returns) will closely track the green line (the forecasted 10-year returns.) As far as a long-run forecasting models go, the Buffet indicator, whilst not the best, is fairly respectable with an r-squared of 0.76 – so it would be surprising if that black line doesn’t hug the green line closely!
There is no model that can directly predict short-term SP500 returns with meaningful accuracy. But you can get pretty damn close (as these things go) by deriving short term returns from accurate long-run models like the Buffet model. It works like this:
1.Find a really robust and accurate long-run (assume 10 year) forecasting valuation model (there are many)
2.Get the 10-year forecast from this model from nine years ago
3. Work out how much the SP500 has grown since this date 9 years ago
4. Subtract (3) from (2) to work out how much upside is left in the year ahead.
In fact we can apply this short-run look-ahead method to any x-year horizon, and this is what we do with the RAVI to compute 1/3/5 year look-ahead forecasts:
The important thing to bear in mind is that the current reading of a long-term valuation model, be it Buffet, Tobin-Q, Shiller P/E, Hussmans Market-cap to Gross Value Add etc. means absolutely nothing to short-to-medium term forecasts. What is important, is what this model was saying 7-10 years ago and how much the SP500 has eaten into those forecasted gains since then.
Now the RAVI long-run model has the following 10-year forecasting profile, which at 0.89 r-squared is pretty respectable:
If we use the methodology described above to forecast 1-year ahead returns we get the following profile. I can tell you, an r-squared of 0.4 on such a short-run forecast is pretty decent as these things go. But quite obviously from the profile, you can see wide variances on occasion:
On a three year look-ahead things become rather respectable in terms of correlations for short run forecasts:
Five-year look-ahead accuracy is pretty remarkable given how close its correlation is to the accuracy of the 10-year forecast:
We can deduce that both the 5-year and the 3-year look-ahead models are forecasting around 4% annual growth for the SP500 from here on, which is pretty low. In fact it becomes pretty interesting to track the average annualized look-ahead forecast of all the models over time:
You can see that as the SP500 has been tracking upwards relentlessly, so the average annual forecasts have been declining of late. But more interesting is that when the average annual forecasts turn negative, it looks like, on the surface, we have a nice recession/bear market warning. Stock market valuation model as recessionary indicator – the idea is appealing!
So there you have it – we are in an era of weak forecasted SP500 returns with all the elevated risks associated with that. You need to decide if we are in a “new paradigm” of prolonged elevated valuations (its quite possible) and take your chances, or de-risk accordingly. Caveat Emptor
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Negative returns for SP500 in next decade
The RecessionALERT Valuation Index (RAVI) has been updated for 1Q17 and for the first time since 1999, is forecasting negative 10-year total returns for the SP500:
The chart on the right shows that the RAVI continues to forecast SP500 decade-ahead total returns with relative accuracy, especially when one considers that the green forecast line has data points that are seen 10 years before the black line depicting actual 10-year returns.
Now there have been a lot of valuation models predicting 10-year ahead negative returns but this does not mean one should be getting out the stock markets. One needs to review the short run (albeit less accurate) forecasts. As we can see below, these still hint at reasonable short run returns of the order of 11% per annum:
In March 2017, we used the 4Q16 RAVI forecast to predict returns for the SP500 to the end of 2017. Our low estimate was 2472, our median estimate was 2,565 and our high estimate was 2,718. As you can see from the RAVI daily tracker on top-right below, we have achieved the lower target and are 3.8% away from the median target and 10% away from the high target with some 5 months still to go:
Here is another interesting way to asses potential short-run returns for the SP500 – look at the US economy as per our Long Leading Index. When this signals recession then we have cause for concern for the stock market
25 most important US economic indicators animated in 1 minute
Here’s something fun we played with after just compiling our June Long leading US Index report for our subscribers
Horrific revisions to HWOL data
The Conference Board Help-Wanted-Online (HWOL) program is closely followed by us to get a feel for the labor market. It is one of over two dozen labor indicators we examine. The monthly HWOL data have been produced by the Conference Board since May 2005, replacing the Help Wanted Advertising Index of print ads, which was published from 1951 to 2008. HWOL data contain the universe count of all ads posted online during a month, with a mid-month survey reference period (e.g., data for October would be the sum of all posted ads from September 14th through October 13th). The HWOL program collects data from over 16,000 online job sources and removes duplicated ads.
The February release incorporated revisions with the following description “With the February 2017 press release, the HWOL program has incorporated its annual revision, which helps ensure the accuracy and consistency of the HWOL time series. This year’s annual revision includes updates to the job board coverage, a revision of the historical data from May 2005 forward, an update of the Metropolitan Statistical area definitions to 2015 Office of Management and Budget (OMB) county-based MSA definitions, and the annual update of the seasonal adjustment factors.”
“Great stuff” we thought. “Some solid revisions to keep us honest”. First up was “Total ads” which are all unduplicated ads appearing during the reference period. This includes ads from the previous months that have been reposted as well as new ads. We had to double-take when we looked at the data. It looked vastly different to the last time we peeked at it. Upon inspections the revisions were downright nasty:
We also look at the “New ads” which are all unduplicated ads which did not appear during the previous reference period. An online help wanted ad is counted as “New” only in the month it first appears. This is more volatile but provides useful comparison data to “Total Ads”. Again this is when that coffee you’re holding spills into your lap as you look in disbelief…ugly ugly ugly.
Both of these metrics are screaming recession from the hilltops – and this just after revisions. Given that the Conference Board is an organisation that’s being doing the data compilation and revisions thing for a while now, this has to be pause for thought. The labor market is not as well as we may think.
Let’s look at various labor indicators taken from our Long leading Index of US Economy:
So these HWOL revisions look rather alarming, but are not being collaborated by many other traditional metrics. Lets look at it again, but in the context of the last recession (grey shading). There’s no two ways about it – butt ugly.
The folks at The Conference Board are onto this though. From their website : “NOTE: Recently, the HWOL Data Series has experienced a declining trend in the number of online job ads that may not reflect broader trends in the U.S. labor market. Based on changes in how job postings appear online, The Conference Board is reviewing its HWOL methodology to ensure accuracy and alignment with market trends.”
The Board of Governers sniffed that something was up in June 2016 already, observing a substantial divergence since the end of 2012 between HWOL and the Job Openings and Labor Turnover Survey (JOLTS), administered by the Bureau of Labor Statistics. From their note : “All told, the average price for Craigslist job ads rose substantially, and roughly doubled since the end of 2012 (Figure 2), coinciding with the period when online vacancy posting as measured by HWOL noticeably underperformed the JOLTS vacancy growth.”
Now that the Conference Board is sanity-checking its HWOL methodology, it’s going to be really interesting to see how this pans out. But can this really be simply blamed on Craiglist prices? I’m not so sure. Surely its not that ridiculously simple?
Our house view is that we may have recently narrowly avoided recession, but are not close to it now. The lessons here are that reliance on single economic time-series, no matter how comprehensive they may appear (HWOL is fairly comprehensive in its number of datapoints), can be dangerous. It also highlights how revisions can wildly swing data with certain economic time series (as opposed to financial data such as interest rates, interest rate spreads, bond yields, credit spreads, corporate bond yields and the like.)
Mixed Signals from Labor Market
We keep getting good news about employment and the labor market. But we rarely see the less optimistic numbers.
Yellen’s Labor Dashboard (see here) is looking strong with all but 3 of the 9 components above pre-recession levels:
An index derived from the percentage of U.S states with rising unemployment looks worrying. Whilst the national unemployment figures seem fine, the population demographics of some large states seems to be masking underlying weakness at a state-level. Again, the broadness of this index makes for worrying numbers:
Here is another way of looking at the state-level unemployment numbers. The black line is derived from simply averaging up the unemployment rate from each individual state whilst the green line is the actual % of states with unemployment rates that are rising.
That means 75% of US States have unemployment levels higher than the best (lowest) numbers yet witnessed.
In summary, it would appear that several large-population states may be enjoying employment gains, but large swathes of the U.S are not.
NOTE : The state-level unemployment metrics shown above are excellent early-warning signals and are included in our highly comprehensive U.S Long-Leading Economic Index
U.S Economy remains vulnerable
It is true that some genuinely troubling signals are starting to make themselves known. Let’s look at some of them.
But by far the most worrying trend, is the labor market, where a broad-based, consistently increasing weakness among the 52 US States is being masked by national numbers being touted about that include high population density states:
The prior chart that shows the average unemployment rate among the 52 states, together with the % of 52 US States with rising unemployment (now at 50%) being in far worse shape than the national unemployment rate below implies:
These are just a few indicators in a battery of twenty-one that we examine, and whilst there are no alarm bells yet, the aggregate composite of all 21 indicators shows the US economy the most vulnerable to exogenous shock since this expansion started:
Unemployment more widespread than thought
The average state unemployment rate seems to be putting in a bottom, a less flattering picture than that painted by the nationwide unemployment rate. But the real surprise comes from the percentage of 52 US states that have rising unemployment, which shot to over 60% for the month of July 2016. The last time this rose to above 60%, was six months before the economy peaked in 2008.
The chart below shows all prior occasions the percentage of U.S States with rising unemployment went above 60%. We see that the percentage of US states with rising unemployment is an excellent proxy for the probability of US recession:
It is clear some close attention is going to have to be paid to this metric over ensuing months. Whilst the national rolled-up unemployment figures are not showing signs of stress, it is quite clear that stresses are building up when examining the individual state data.
The percentage of U.S states with rising unemployment is but but one of 21 indicators we use for our Long Leading US Economic Index.
NBER’s Big-4 Indicators had a narrow miss
Reading through all the positive press about jobs numbers and so forth, its hard to comprehend that the 4 main indicators used by the National Buro of Economic Research (NBER) to determine US recessions, had a narrow miss recently.
If you recall from our popular 2012 article, the NBER does not define a recession in terms of two consecutive quarters of decline in real GDP. Rather, a recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales.
With regards to the monthly data, they examine 4 monthly co-incident indicators:
- Industrial Production
- Real personal income less transfers deflated by personal consumption expenditure
- Non-farm payrolls
- Real retail sales deflated by consumer price index
If one takes the month-on-month percentage change of these four data series, and combine them into a standardized average composite, you get the below, where we had negative prints for February and May:
We can also count the amount of times the non-smoothed month-on-month indicator in the first chart has dipped below zero in a rolling 4-month period. When this count reaches 2 we are in a “danger zone” and when it hits 3, we are most likely already in a recession. We hit the danger zone in May and June. Lets call this count reaching three Syndrome-Two.
Another interesting thing to note is how many consecutive negative prints the indicator in the first chart has made. When this hits three we are also most likely in a Recession. Lets call this event Syndrome-Three:
The below Syndrome count shows how many of the three syndromes are present at any time. If two or more syndromes are present, we are most likely already in recession.
We run a more sophisticated algorithm for the Big-4 for our subscribers, as described in this robust tested methodology, and here are the probabilities of recession from the model:
To conclude, looking at the individual co-incident monthly data used by the NBER shows a far more pessimistic view currently than when looking at a syndrome of conditions. But the co-incident data in this particular indicator and the recession probabilities we are registering are not as bullish as the employment data would have you think. In fact, taking our proprietary implementation of the Big-4 index, and comparing it to the last 8 expansions, shows just how meek this recovery has been:
There is one final interesting observation though – for the first time this expansion, the co-incident data is coinciding with the weak leading economic data made from 21 leading data series and first witnessed for February 2016. And that’s something worth pondering about.
Number of countries with back-to-back negative quarterly GDP prints is rising
The percentage of 41 OECD countries around the globe that have just posted a negative 1Q2016 on the back of a negative 4Q2015 (old fashion technical recession) has started to rise. Its nothing to be concerned about just yet but the rise itself, although shallow, is something worth watching as 2Q2016 numbers start coming out.
U.S Economy most vulnerable to any shock since 2008
The #Brexit vote caught the consensus view off-guard and stock markets, currencies and commodity prices have made large responses. This may be bringing up thoughts if Brexit could be the external shock that marks the decent of the U.S economy.
Whilst we will not entertain making predictions on this complex matter, we can however state that the U.S economy is the most vulnerable it has ever been since the 2008 financial crises. This is at least according to our composite of 21 leading indicators as at May 2016 shown below:
From the green diffusion line in the chart above we note that more than 50% (eleven) of these indicators are now in recession territory, with the overall (black) composite hovering near contraction (the red line). Eleven of the 21 indicators are long-leading and ten are short-to-medium leading. Six (60%) of the short-to-medium leading indicators are in recession and five (45%) of the long-leading indicators are in recession territory.
Visitations of the green diffusion index to the 50% mark and of the black composite to the red zero mark have been frequent in the past without leading to recession, but the fact of the matter is that these are periods of extreme vulnerability of the economy to internal or exogenous shocks or geopolitical events. With no margin of safety, or buffer, it doesn’t take a very large shock to tip many of the indicators past the point of no return, resulting in contagion to other indicators and finally the whole economy.
Recession Probability Roundup : Elevated levels
NOTE : This is a subscriber-only article that was made open for public viewership on 20 May 2016.
A probability of recession of 60% most certainly also implies probability of no recession of 40% and of course this is related directly to any false positives that may (and have) occurred in the past (see March 2003 in above chart.) You will note from the Headwinds chart on top of page 8 of the most recent SuperIndex report that we make a recession call only when the headwinds index exceeds 5, since this is the level that has produced the least false positives in the past. Only when this occurs, does the Headwinds index add one point to the Recession Forecast Ensemble (RFE)
Also, the Headwinds index, due to its stylized (as opposed to numeric) nature is very lumpy and thus recession probabilities can jump by large amounts moving from one headwinds level to the next. So whilst this model is showing elevated recession probabilities in 9-14 month time it does not constitute actionable information at this juncture.
The HeadWinds index is but one of the 6 models in the RFE and that is precisely why we designed the RFE – to allow for input of multiple robust diversified models into determining recession. Even if the Headwinds index was to move to 6 and the model were to call recession, the RFE would only rise by 1, which still does not constitute actionable information other than maybe some modest hedging or de-risking activity.
Another way to look at the RFE is to average the current recession probability showing on each of its six model components, which is currently showing a 14.6% probability of recession. This model appears to have served well in the past, with zero false positives above readings of 0.20.
It is therefore true that recession probabilities are elevated, the highest they have been since this expansion started in 2009. In fact, we have been witnessing this quite emphatically with non-RFE components, with both the Weekly Leading Economic Index (WLEI) and more importantly, the new and comprehensive US Long Leading Index.
But as the chart above shows, elevated probabilities only becomes concerning once the average RFE component recession probability is above 0.2.
It is our suspicion that the recession probabilities will abate in coming months, purely based on the recovery we are seeing in the WLEI and the March bounce we saw in the new Long leading Index report published last month to the USLLGI reports tab. This leading index has over 50% of its 21 indicators in recession territory (green line below).
Over 30% of States with rising unemployment
The useful thing with this breadth metric is that deterioration in unemployment is made visible long before it shows up in the average national unemployment rate. Whereas the national unemployment rate is at best a co-incident indicator for recession, the percentage of states with increasing employment acts as a reliable leading indicator – reliable enough to be added as one of the 21 components to our New Long leading Indicator.
The deterioration in this unemployment statistic is corroborated by several others we covered in a blog posting last week, titled “Labor market not as strong as you think“.
Labor market not as strong as you think
The strength of the labor market is constantly being trotted out in defense of the robust status of the US economy, but broad sets of labor data show this not to be the case.
This indicator needs to fall below -10 before the odds of recession skyrocket to a near certainty and so whilst there is no cause for immediate alarm, it is clear the indicator is not exactly shooting the lights out and has probably peaked. As it is a leading indicator, it is telling us that further gains in the labor market are likely to be muted going into Summer.
This indicator needs to fall below -5 before the odds of recession skyrocket to a near certainty and whilst there is no cause for immediate alarm, it is clear the indicator is not exactly shooting the lights out either and has definitely peaked. As it is a leading indicator, it is also telling us that further gains in the labor market are likely to be muted going into Summer.
The Conference Board also publish a “Help Wanted Online” (HWOL) index, that tracks Online advertised vacancies. Whilst the data has a much shorter history, it clearly peaked well before the 2008 Great Recession and has most certainly peaked in mid 2015:
This indicator has a long term history going back much further than that shown and is a reliable indicator of recession when it starts rising, although it is a co-incident and not a leading indicator. Whilst it appears to have leveled out recently, it certainly shows no cause for immediate alarm and cannot even be categorized as having peaked as with the other three indicators we showed.
Over 25% of the 52 US States now have rising unemployment, a record for the current expansion. Again, nothing to be alarmed about until this measure gets to over 50% (where it serves as a leading indicator of recession) but you have to agree it is far less flattering than the national unemployment rate measure would have you believe and has most certainly “peaked” in mid 2013.
So let’s have a bit of fun here, and create a “Super Composite” dynamic factor model combining all the Labor indicators discussed above bar the HWOL which has too short a history. So this is the 19-factor dynamic factor model plus the Conference Board ETI plus the U.S Civilian Unemployment rate plus the % of states with growing unemployment:
The above model has a very high 0.998 correlation (Area under the curve, or AUC) with US economic contractions and expansions when using -5 as the contraction/expansion trigger but is just as useful using the zero trigger level, which just happens to provide a 4-6 month lead to recession.
The bottom line is there are no alarm bells for recession according to the labor indicators just yet, but the labor market growth certainly looks to have peaked and is indicating far less strength than is commonly thought. In fact, leading indications are that labor market gains are likely to be muted going into Summer.
Animation : The incredible US employment recovery
Below is an animation of the annual average unemployment rate per U.S state from 2011 onward. It’s quite incredible to see how unemployment was erased state-by-state over the years:
However statewide improvements in employment have probably peaked-out as shown in the chart below, which depicts the aggregate (equal weighted) inverse 6-month unemployment rate growth for each of the 52 U.S States together with a diffusion showing the percentage of 52 U.S states with increasing unemployment. You can see that recently the diffusion has been rapidly rising showing that unemployment is starting to increase again in the 52 U.S states.
How can we forecast 30% upside for 2016 with RAVI?
The RecessionALERT Valuation Index (RAVI) is currently forecasting 30% growth for 2016 for the SP500 with its 1-year forecast model. “How the heck is this possible given current overvaluation of the market?” we can hear you say. Let us show you how this is calculated so we can put the forecast into context:
We can see that 10-year forecasts for the SP500 Total Return Index (including re-invested dividends) has a correlation of 0.92 to actual achievements. This is aptly shown by the chart on the right which plots the forecast (green line) and the actual achieved 10-year return (black line). Its important to note that the green line is seen 10-years before the black line!
The 10-year model is currently forecasting 121% growth from Dec 2015 to Dec 2025 or approximately 8.3% compound growth per annum. So one way we can forecast returns for the year ahead is to say that we are projected to grow 8.3% compound per annum so that means we can pencil in 8.3% growth for 2016. But this is a very broad brushstroke since we all know the market is highly unlikely to grow steadily by this amount for 10 years in a row.
The other way is to look at what the 10-year forecast was 9-years ago and then compare this to the actual growth we have achieved in the last 9 years and from this we should be able to work out how much upside is still left in the year ahead to make the 10-year forecast made 9 years ago true. In fact we can apply this logic to any look-ahead horizon:
Using the above table, if we set x to 9 we get the procedure used to forecast 1-year ahead returns. Similarly, setting x to 8 we can impute 2-year ahead returns.
Before we go ahead, let us just state that a correlation coefficient of 0.48 for a model forecasting 1-year ahead stock market returns is absolutely amazing, especially when considering that direct correlations between most commonly-used valuation metrics and 1-year Sp500 returns range from 0.1 to 0.15. But as you can see from the 1st chart above on the right, even this model can vary quite significantly from quarter to quarter (look at the vertical distances achieved between the green line and the black line). So you have to understand that these 1-year forecasts are rough guidelines and an achievement of 10% gains against a forecast of 30% gains is still “within target” of the model.
So let’s go through the logic in a real world, current example. At the end of December 2006, the RAVI model was forecasting 113.35% growth for the next 10 years to December 2016. As at the end of December 2015 the SP500 Total Return Index had grown 63.3% since this forecast was made. Using a mathematical formula we can thus deduce there must still be 30.6% upside left in the SP500 to bring the 10-year growth to 113.35%
The conclusion therefore, using this model, is that the stock market has not yet run way ahead of itself – contrary to what most people are trying to make you think.
The reason most people think the market has run ahead of itself is that everyone keeps trotting out valuation metrics like Shillers’ 10-year CAPE, Tobins’-Q, Market-Cap to GDP (Buffet Indicator) and so forth and these all look more or less the same and the messages being imparted are all more or less the same – the valuation metrics are at all time highs or way above long-run averages and therefore 10-year future returns are going to be poor and the market is in a dangerous period:
The fact of the matter is that these valuation metrics all have very little bearing on short-run returns and what is more important is:
1). What they were saying nine years ago and
2). How much the stock market has run since then.
This does not detract from the fact that the metrics are way above their long-run averages – they clearly are. But this is creating a problem for 10 years time, and not now.
Below is the best short-run correlation we could find directly from all the valuation metrics, obtained with the Market Cap to GDP ratio. As you can see, the correlation provided from using the metric directly is far short of the 0.48 achieved with the RAVI using the “look-back” methodology we described in this note.
Nonetheless, we now have several estimations we can use for 2016 returns for the SP500:
a). 8.3% if we annualize the 10-year forecast (2,213 target)
b). 12.4% if we annualize the 5-year RAVI forecast (2,297 target) <-most likely scenario
c). 20.7% if we annualize the 3-year RAVI forecast (2,467 target)
d). 27% if we annualize the 2-year RAVI forecast (2,595 target)
e). 30.6% if we use the 1-year RAVI forecast (2,669 target)
We tend to ignore the 1-year RAVI (due to its wide fluctuations) and use the low, average and high from (b),(c) and (d) above for 2016 targets on our daily updated dashboard:
Bear in mind that the 3 radial gauges change on a daily basis depending on the current level of the SP-500 and how far it needs to go to reach the LOW,AVG and HIGH targets.