This note was originally published at 8am on January 25, 2013 for Hedgeye subscribers.
"Most of the time ‘I don’t know’ is the right answer.”
-Wesleyan University Professor, 2008
It was nearing midnight and towards the end of a ten hour stint in a 4’ x 8’ freezer on a winter night in early 2008 that I realized I wasn’t going to be a career research doctor.
At the time, I was a PhD candidate performing an RNA isolation as part of some larger work on DNA enzyme kinetics. What that means exactly isn’t really important, but it takes a long time and the work flow requires that most of that time be spent inside a walk-in ice box.
Tired, bored, and numb, I was thinking more about the puts I bought in NLY (REIT/Mortgage Investor) earlier in the day than on the final mix components for the experiment. I ended up aliquoting (fancy science term for “added in”) way too much of the wrong substrate into the mix. No take-backs or mulligans in experimental biochemistry - Game over, reset clock, 10 more hours of overnight freezer duty. A short-time later I joined Hedgeye.
Similar to probing the populous on their view of functional Enzyme Kinetics, I imagine that asking the average person how the U.S. calculates inflation conjures images of Good Will Hunting scenes, blackboard equations and chalk dust mathematical revelation.
Reality, however, more often resembles a bearded, middle-aged Robin Williams than it does a svelte, young Matt Damon. Consider the following question:
“If someone were to rent your home today, how much do you think it would rent for monthly, unfurnished and without utilities?”
That gem of a perfectly subjective question, asked as part of BLS’s monthly price survey process, drives the calculation of “owner’s equivalent rent” and singularly represents approximately one quarter of the index used to calculate CPI inflation in the United States.
China reports official GDP numbers 5 minutes after the quarter ends, we have owner’s equivalent rent. Next.
Parsing the reality from the illusory in reported domestic labor market data and understanding the subtleties of the seasonal and other statistical adjustments presents its own unique challenges.
‘Tis the Season(ality)
As we’ve highlighted previously, strong and quantifiable seasonal adjustments have had a meaningful impact on the temporal trend in reported economic and employment data over the last four years. In short, the shock in the employment series in late 2008 – early 2009 which occurred alongside the peak acceleration in job loss during the Great Recession has been captured, not as a bona fide shock, but as a seasonal factor.
The net effect of this statistical distortion is that seasonal adjustments act as a tailwind from September – February, then reverse to a headwind over the March-August period. From a positive seasonal adjustment factor perspective, we’ve got about one month left.
Sell in May & Go Away (Until September)
From a strategy perspective, the temporal pattern in market dynamics, despite being rather obvious to any market observer, hasn’t been insignificant.
The annual déjà vu pattern in market prices, reported economic data and monetary policy announcements observable over the last 4 years isn’t particularly surprising when considering the reflexive interaction between the associated dynamics:
reported economic data begins to inflect, market prices move higher, confidence and optimism measures begin to improve alongside stock prices and reported econ growth, the marginal bid moves from treasuries to equities as improving conditions pull expectations around a Fed exit timeline forward, equities benefit further while the reported data continues to confirm - until it doesn’t - and then the dynamics reverse, culminating with a new QE announcement in late Q3 just as the data and seasonal adjustments impacts hit trough.
Compressed economic cycles and amplified market volatility at its statistically distorted and centrally planned best.
As the domestic employment and housing data has continued to confirm our 1Q13 Macro Theme of #growthstabilizing, a risk management question we’ve been considering is the possibility of seeing a 6-handle in the unemployment rate in 2013. With Bernanke offering an explicit employment target of 6.5% for a cessation in QE initiatives, a significant decline in unemployment over the NTM may augur higher yields as the bond market attempts to front-run a prospective Fed exit.
A material, mean-reversion back-up in yields is of obvious import for asset allocation decisions and remains the principal candidate catalyst for a driving a large-scale rotation to equities.
Further, in so much as an end to money printing is dollar bullish, we could see a perpetuation of the USD Higher --> Energy/Commodities lower --> Real Earnings/Real Growth Higher dynamic we think needs to persist for sustainable real consumption growth. A step function move lower in commodity prices would also be equity supportive from a rotation perspective.
In a recent analysis we framed up the variable dynamics and put some quant around the magnitude of change in the relevant unemployment rate drivers necessary to take unemployment below 7.0% over the NTM (email us if you’d like a copy of the note).
In short, while we wouldn’t necessarily view a 6-handle on the unemployment rate by 2013 year-end as our baseline case, the reality of the math suggests that it wouldn’t take extraordinary improvement in the factors that drive the unemployment rate to take it below 7% over the NTM.
In terms of how we model unemployment, we effectively need to see 2 of the 3 input variables to trend favorably with respect to their impact on the unemployment rate.
For example, scenarios in which Employment Growth accelerates a reasonable 20bps (2Y basis) on average in 2013 and growth in the Civilian Non-institutional Population (CNP) declines linearly to the historical average over the NTM or the Labor Force Participation Rate (LFPR) continues to decline at the 3Y CAGR both result in a move to/below the 7% unemployment level in 4Q13.
In the chart below we provide a timeline view of the 2013 Unemployment Rate under a selection of progressively favorable scenarios. If you’d like to observe the impact of your own growth and participation rate assumptions on the unemployment rate timeline you can link to the associated model here >> Unemployment Rate Variable Analysis_HEDGEYE
Yesterday on CNBC Ray Dalio remarked that the question for investors now, as always, is how events will transpire relative to what the market has discounted.
We continue to like our 1Q13 Macro themes of #growthstabilizing and #housingshammer. Ultimately, however, Investment perspective remains wedded to last price. Now a hundred SPX points higher from where we first penned the #growstabilizing hashtag back in early December, the relevant risk management question is whether growth can organically and sustainably accelerate from here.
On my first day in the freezer in grad school, my professor offered the following piece of memorable advice with respect to evaluating one’s place within the program’s intellectual pecking order:
“Regardless of what they say, nobody really knows anything. Most of the time ‘I don’t know’ is the right answer”
Our immediate-term Risk Ranges for Gold, Oil (Brent), Copper, US Dollar, EUR/USD, UST 10yr Yield, and the SP500 are now, $1654-1679, $111.51-113.95, $3.65-3.71, $79.41-80.14, 1.32-1.34, 1.84-1.91%, and 1481-1502, respectively.
Christian B. Drake
McDonald’ is set to release January sales tomorrow before the market open and, while management already guided to negative global comparable sales, we will be watching for any indication of how respective markets and menu items are performing in the respective geographies.
Since the company reported FY12 results recently, we will keep this note brief. McDonald’s shares have been performing strongly of late but lag the S&P 500 over the past week as we move close to January sales being announced. We remain convinced that the Street’s projected acceleration in earnings in 2013 is overly optimistic and would avoid buying the stock at these levels.
The charts below illustrate what we believe the investment community will perceive as good, bad and neutral results for the US, Europe, and APMEA January sales.
Takeaway: Let's keep the debate alive on the root of a key issue and map out the timing/impact of shop-in-shops. Like JCP or not, let's do some math.
Bulls and bears don’t agree on much with JCP, but they probably would agree that the trajectory of the company’s top line is the base-line of success or failure. Even bears focused on (a compelling) liquidity case need to agree that liquidity is not an issue if top line shows up to the party.
That said, we find it surprising that in most discussions we have on JCP (both bulls and bears) the comp assumptions people use are often seemingly pulled out of thin air.
We concur that no one has a crystal ball on comp trends, including us. But the reality with JCP is that we have enough pieces of the puzzle to come up with an algorithm for how aggregate sales per square foot should change simply as higher-productivity shops are layered in over the legacy layout.
When we go through the math, our model suggests the following results:
There are some important modeling variables:
1) JCP end this year with 9 shops, adds 30 next year, and then evenly weights the remainder through the end of 2015.
2) JCP touches 75,000 square feet, or about 69% of JCP’s total.
3) Out of the 100 shops, there are a) 3 ‘Stores’ which measure 2,000 feet, b) 85 ‘Shops’ at 750 feet, c) 12 ‘Boutiques’ at 300 feet, and d) a town square at 2,000 feet.
4) Shop additions are timed by month such that JCP maximizes its floor space during peak times of year. For example, during September and October 3-5% of the stores will be under construction, but only 1-2% will be renovated during holiday periods.
5) As it relates to productivity, we assume that…
- For the full quarter stores under construction, that productivity get cut by 75%. While the space is being worked on, there is actually zero productivity, but on the flip side it is not usually closed down for a full quarter.
- Square footage that is altered gets a 15% comp lift. Remember that out of the eight shops opened thus far, there’s been just over a 30% lift. But this is far from uniform. One space went from $69/ft to $207/ft. Another went from $93 to $160/ft. But you don’t need to be a genius to figure out that to have this kind of productivity change and average out at only 32% in aggregate, there had to be a couple of stinkers bringing down the average. Fortunately, those are the JCP branded shops and Arizona – two incumbents at JCP where the company mis-executed on fashion. From our perspective, we’re not too concerned here. We’re more excited to see the impact of shops from brands that will do the merchandising along with JCP – brands such as Nike, Giggle, Carter’s, Joe Fresh, Martha Stewart, Tourneau, and Bodum. We don’t think it’s too great of a stretch by any means for us to model a 15% lift with concentrated efforts by brands like this coming on.
- Unaltered square footage comps at zero, BUT…
- We add a ‘dysfunctional pricing’ quotient, which allows us to adjust the comp down for a good part of the next year while the company figures out its pricing strategy. Note that our model assumes that the negative impact that we’ve seen – and should continue to see in 2013 – ultimately goes away, but never reverses course and helps the model on the positive side.
6) We know that this might seem like a completely ridiculous exercise today given that the topic du-jour is how the company can prevent using its credit line, defaulting on debt and continue store roll outs while it is comping down 35%. Bears will immediately come back and say that if sales trends persist, then store rollouts need to slow, or halt -- which is a game changer. But if the company can pull through, fund its expansion, and execute on its shop-in-shop strategy as outlined in this model, the math tells us that we could be looking at positive comps in mid-2013, mid-single digit comps throughout 2014, and low-double digit numbers by the end of the rollout.
Again, we appreciate the heated debate on this name, and concur that that there’s a lot that could go wrong (remembering that we were bearish with the stock in the $30s). But regardless as to where you stand on this one, let’s continue to flush out the facts, and keep the debate alive.
Call with any questions.
Here’s the math behind our ‘shop-in-shop’ accretion analysis.
Today we shorted Gulfport Energy (GPOR) at $41.57 a share at 9:44 AM EDT in our Real-Time Alerts. We'll get a better price than the CFO did selling stock. Shorting high on green in Hedgeye Energy Sector Head Kevin Kaiser's Best Idea (short side). Ask sales at Hedgeye dot com for our bearish slide deck from yesterday's Kaiser call.
Hedgeye Director of Research Daryl Jones appeared on CNBC’s Closing Bell Exchange today to discuss the markets and the direction the US economy is headed in. Daryl lays out our thesis on what’s driving the economic recovery, including the housing market and how it drives consumption. You can watch the full video clip above.
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