This is the first in a series of Retail Macro reports where we take a deep dive into some of the key sector-specific Macro factors that are driving fundamentals. We’re firm believers that you can’t simply look at POS data or a stream of made-up government data and use it in an investment process. Well, I guess you could, but you’ll probably lose money rather consistently.
Today combine everything from company-reported information, to bills of lading on imports, to specialized government office receipts to BEA/BLS data to tell us the truth about what’s happening with product costing and pricing behavior, how it’s impacting margins, and ultimately how it is represented in the stocks. We’re not going to make a sweeping macro call, but what we will do is isolate the key questions that need to be asked and answered in considering how inflation is impacting the dollars a company pulls out on its gross margin line. Note, there are 8 Exhibits in this report that are critical in understanding our analysis. If, for whatever reason you cannot view them, please reach out to us directly.
The unmistakable math is that we are on the wrong side of incremental change in sourcing/pricing's impact on the supply chain. We're currently at a run-rate of about $6bn per quarter. That might not seem like much in a $280bn industry. But keep in mind that the $280bn is revenue. It has 10% margins -- at best. So we're talking $6bn accretion to a $28bn margin figure. Something tells us that the CEOs in this business are not looking at the math like this, and are not asking themselves if it is sustainable.
As we are squarely in the period where Average Unit Cost (AUC) is down while Average Unit Retail (AUR) is presumed to be holding up, we want to consider the following analytical build-up…
1) Get The Cost Component Right. In looking at the cost component, PPI is irrelevant. That encompasses the apparel that is produced in the US, which is less than 5% of what we purchase. We need to look at the actual import cost, which is released by OTEXA (the Office of TEX and Apparel). But to take it a step further, you cannot simply look at PPI vs. CPI. ‘All percents are not created equal’. The simple fact is that the average unit retail is about $11, while the average unit cost is about $3.50. On an apples to apples basis, 1% of the former = about 3% of the latter. In other words, cost can be up 15%, or $0.53 per unit, and all it takes is about a 5% change in Price to generate $0.55 to offset the cost increase.
Exhibit 1) Need to look at dollar value spread between cost and price instead of percent change in both.
Source: BLS, OTEXA and Hedgeye
2) Aggregate Supply Chain Impact Is Easy To Gage. When we can isolate the units shipped and the cost/price per unit, we can break down the economics of selling pretty easily. Specifically, we can gage the price/cost spread, and quantify how much money is either being inserted in, or detracted from, the apparel supply chain. We like to look at the 3 month trend, which shows prior 2-month shipments vs. current month retail. This appropriately accounts for the lag through which product is clogged up in the supply chain.
Exhibit 2) As is clear in this chart, we’re still clearly in the green as it relates to positive margin impact on retailers, but the trend has decidedly turned negative.
Source: Hedgeye
3) The 12-month trend is even more stunning. We’d argue that it is not as relevant as it relates to modeling immediate-term margins for the retail supply chain (i.e. retailers, brands, manufacturers), but as it relates to playing the BIG Macro trend where the group meaningfully outperforms and you make money regardless of what you own – that trade is probably done.
Source: Hedgeye, Factset, BLS, BEA, OTEXA
4) The Stocks Recognize This Relationship. To prove the point, let’s look at the relationship between the S&P Retail Index (RTH) and the Spread between Consumer Price and Retail Cost. The relationship is quite strong – though there have been certain points in different cycles when the market discounted the impact of inflation/deflation at different times. But the discounting mechanism was clearly and definitely there.
Source: Hedgeye, Factset, BLS, BEA, OTEXA
5) Reversion To The Mean is Likely, And Is Net Bearish. This one gets a bit more complicated. But in essence, it shows the change in the RTH compared to the change in the price/cost spread. In effect, as the line goes down, it means that either the market is underperforming the inflation spread, meaning that either a) the RTH is underperforming the inflation spread, or b) that the inflation spread is getting positive, but that the market does not care. There have been several notable moves, which are outlined below. But the punchline is that we just came off a 2-stage process whereby 1) both the stocks and inflation spread worked simultaneously, and then 2) the group stopped working, but it did not go down. Inflation spreads caught up partially to the already realized price performance, but to revert to the mean, spreads need to get meaningfully better, or the stocks need to head lower.
Source: Hedgeye, Factset, BLS, BEA, OTEXA
6) Triangulating The Data…With More Data. Let’s slice and dice the data another way by triangulating it with container imports. Theoretically, if we know a) the average price per container at cost for every box coming into US ports, which we do (about $57,000 per container), and b) the average cost per garment coming into the US, then we know one of two things… 1) The change in mix, or 2) the load factor (the amount of stuff crammed into the box). Almost always, the difference is the load factor (which currently sits at about 16,300 garments per box). For the record, that ratio fluctuates greatly by category – women, men, footwear, athletic, baby, underwear etc…, and we have all that data and will have a future Macro Deep dive on it.
What it shows us is that as unit costs rose due to the commodity bubble, shipment value actually came DOWN. We know that mix between subcategories did not erode during that time period, not did average price inter-category. What that tells us is that the Load Factor came down significantly. Remember that if you own a factory in Asia and you ship a container every Monday and Thursday, you don’t cancel the box simply because there’s less stuff to go into it. You ship it, but with less product in the hold.
Source: Hedgeye, PIERS, OTEXA, BEA, Company Documents, Factset
7) Strength Could Take Margins Higher. When we look at this Load Factor versus aggregated margins for every company in the industry, the relationship is simply unmistakable. The only problem for margins is that the Load Factor change is at peak. We agree that it could head higher from where it is today…
Source: Hedgeye, PIERS, OTEXA, BEA, Company Documents, Factset
8) But It HAS To Print Those Numbers To Satisfy The Bulls From Here. The market recognizes this relationship just as well as we do. Unfortunately, it suggests that the Load Factor change will remain about where it is today, or better, and in no way discounts that we could see a slowdown in unit shipped.
Source: Hedgeye, PIERS, OTEXA, BEA, Company Documents, Factset
