Information and data are only part of the picture as it pertains to formulating strategy.
Interpreting data—and knowing what others expect—is also important.
Expectations are constantly in flux, based on myriad influences.
How data compare to these expectations is a critical driver for stocks.
We’ve often said information and data are only part of the picture as it pertains to formulating an investment strategy—equally as important is the interpretation of said data. US May retail sales illustrated the point rather well on Tuesday.
It’s rare one data point directly and solely causes stocks’ movements on a given day (there’s usually far more influencing stocks than just one piece of information). But when you square headlines that far and wide told of the first negative month in US retail sales in nearly a year with the fact stocks surged, you might be scratching your head. Whatever the degree of influence on Tuesday’s results, this is an instructive reminder: It is not only absolute headline data that count, but also how they relate to expectations and how underlying data appear. So let’s apply this logic to the retail sales data in question:
Headline May US retail sales fell -0.2% m/m (+7.7% y/y) versus an expected -0.5% m/m. Headline retail sales fell less than expected—a positive surprise.
Excluding autos (which likely showed the continuing influence of the Japanese disaster’s supply chain disruptions), retail sales rose +0.3% m/m (+8.2% y/y) versus an expected +0.2% m/m. Positive and better than expected.
And for those who assume high gas prices inflated sales, retail sales excluding autos and gasoline rose +0.3% m/m (+6.2% y/y) versus an expected +0.2% m/m gain. Again, positive and better than expected.
Mind you, whether or not an economic statistic exceeds or misses consensus estimates doesn’t imply much about the future direction of the economy (though recent headlines might make you think otherwise). But it is impactful for markets. Stocks’ direction (and the degree of movement) can be heavily influenced by expectations.
Estimating short-term economic results—an important ingredient in setting these expectations—is nearly always a difficult task for analysts. But in recent weeks, that’s been further complicated due to the impact of Japan’s earthquake, which is in no small way responsible for recent data missing estimates. Analysts simply didn’t have a very accurate way to account for potential impacts (like supply chain disruptions) on certain statistics’ growth rates. Some went far—others not as much—and in the end, many consensus estimates proved a bit lofty for May. But after the initial rounds of data arrive, expectations become easier to set. After all, Japan’s earthquake was basically a one-time event—there’s no ongoing overhang of earthquakes now to affect future expectations. Today, the principal question is: How quickly will the supply chain recover? Each analyst will have his or her own estimate, but it’s far easier to assess now than it was a month or two ago, when fewer supporting data points existed to help.
At a broader level, year-over-year comparisons are also far harder now than at this time last year. This is one key pillar underpinning our expectation of a mediocre year for broad stock index returns. Consider: In June 2010, year-over-year comparisons looked back to the recession’s depths. These comparisons now are to a far higher point—a harder hurdle to clear.
As data come in—some above estimates and some below—it’s likely analysts scramble to reset for the next go-round. That can be either good or bad for the short-term future. In some cases, lowering estimates is a plus, since data then have an easier time providing upside surprise if they top analysts’ lowered views. For others, increasing estimates can have the opposite effect. This gap between expectations and reality is a primary determinant of market conditions ahead. So when formulating investment strategy, it’s not solely your take on reality that matters. It’s as much about what investors (overall and on average) think reality is and how the data compare.