A new ecosystem of fear-mongering analysts is using low-quality data to push a narrative of stock market doom and recessionary gloom. iStock; Robin Phelps / Insider
In addition to being the talking heads on cable news, Wall Street analysts and economists serve an important purpose in helping to guide the financial decisions of a wide range of investors—from average Americans to seniors worried about retirement. Institutions have to decide where to invest billions of dollars. At its best, Wall Street’s coterie of experts should translate the shifting sands of the economy into a solid, fruitful investment scene.
I have been a practicing economist for more than 15 years, helping clients and the public understand the shape of the economy and what it means for the markets. I’ve always tried to be clear and allow the data to shape my thoughts rather than fit my preconceived notions. But over the past few years, and especially since the start of the coronavirus pandemic, I’ve noticed that more and more analysts are using low-quality, prepackaged narratives to fuel fear about the direction of the economy and the stock market. are counting on. Sure, stocks haven’t performed well over the past year, but instead of providing a clear outlook to clients and the general public, a new ecosystem of hackneyed, dangerous analysts rely on low-quality data to steer people away from stable investments. are trusting. In an alternative ecosystem of products of questionable quality.
lies, damned lies and statistics
Data is at the heart of any economist’s work; It provides important insight on the state of the economy and can be useful in helping to predict what will happen next in the markets. But there are certain types of data points that investors should look for when trying to identify substandard analysis. In many cases these data points appear to be sophisticated or a perfect catchall, but in fact they present a misleading or overly simplistic picture of the economy.
One type of data point to be wary of includes vehicles for confirmation, which use older data to confirm what an analyst already believes. Take the popular Leading Economic Index. The idea behind the LEI is simple: It combines a range of different economic data points and tracks whether they are getting better or worse – promising to signal upcoming turning points in the business cycle. But all the information in the LEI is already out of date. Individual data components are released a few days or weeks before the overall index is published. For example, the most recent LEI was a sum of data from December, which an analyst might point to as a sign of an impending recession. But if you look at the January data, we’ve already seen positive signs, so there’s good reason to expect a bounce in LEI this month — confirming what we already know.
The index is also revamped after each recession – given the new weights and components, the new index is more fully indicative of the recession that just happened. But if you go back and look at the LEI before each downturn, it usually doesn’t indicate a clear peak. Rather than being a useful measure to gauge the future health of the economy, the LEI is simply an index built around the latest downturn that provides scare signals based on older data.
Other hacked data points are intentionally vague or broad to the point of abstraction. Take another popular freak-out indicator: the monetary aggregate, how much money, such as cash and bank deposits, is floating around in the economy. The aggregate is portrayed as an absolute summation of the economy – what better way to track your health than by measuring all the money changing hands between government, businesses and households? But the correlation between money growth and the health of the economy has broken down over the years, and scaremongers have been able to fit any change in the dollar supply into whatever narrative suits them. Weak money growth is a problem, he argues, because fewer dollars circulating around the economy may indicate that the system is seizing up. On the other hand, a rapid increase in the money supply has been used by doomsdayers as a sign that the only thing supporting the economy is the Federal Reserve handing out new dollars. When a measure is so broad that it chooses its own adventure, it is hardly useful for quality analysis.
The third type of well-known, often-suspicious tool is the highly volatile indicator. These data points are prone to large swings that generate lots of false signals but make it easier for analysts to warn of impending catastrophe.
Take the ISM Manufacturing PMI, which has a legendary reputation on Wall Street. The ISM is convenient to use: It is released at the beginning of the monthly data cycle, tracks the ups and downs of the economy roughly, and is easy to understand. The ISM is a survey of 300 purchasing managers from a wide variety of manufacturing companies, who are asked whether conditions are better or worse than in the previous month. Are customers ordering more or less? Is it easy or difficult to find workers? Are the prices for parts high or low? If the index falls below 50, things are getting worse; above 50, things are improving.
Let’s assume that the ISM signals a turning point in the business cycle when it moves below 50 for three consecutive months. Even with that wide of a reading, the ISM sends out more false signals than correct ones. For example, in the 1990s there were several dips in the ISM below 50 that lasted more than three months and did not result in a recession. In fact, the ISM is three times as likely to signal a trough as it is quick to signal a peak. It is not a recession predictor. Also, the ISM is only telling us about the momentum of the economy. If GDP growth is 4% in January and 4% in February, the ISM will be 50 because things have stayed exactly the same. is that bad? No, 4 per cent growth is good enough. But ISM will be interpreted by some to mean that the economy is on the verge of a major recession.
Ultimately, statistics are only as good as the analysts and economists who use them. Sifting through hundreds of indices, surveys and economic measures requires a discerning eye and a willingness to be humble about the unknown.
One of the most basic principles of statistics is that correlation does not imply causation. Most things in the macroeconomics are correlated. When people are getting more jobs, it usually means that businesses are selling more products. Just because two lines on a chart are moving in the same direction doesn’t mean they explain everything that happens in the wide world.
I can’t tell you how many analysts I’ve seen in this industry who believe they’ve stumbled upon something unprecedented by publishing a chart of industrial production — a measure of how active factories are — against the ISM — which then It is a measure of how factory executives feel about the economy. In many cases, people who break everything down into one or two “simple” explanations have a conclusion in mind first.
A lot of this low-quality research is also heavily skewed toward a negative bias – the economy is getting worse, markets are about to collapse, a recession is on the way. Research shows that humans are hardwired to think that negativity sounds smarter, and the financial media is also inclined to focus on the downside. This creates a perverse incentive for lazy analysts to relentlessly beat the drum of doom and gloom. This also makes it difficult to discount them. In a bull market, when rising stocks lift all boats, these analysts are still making money arguing the negative “isn’t done yet.” And when the market turns and stocks sink, they can shout “I told you so!” I win; You lose your tail.
Ultimately, the people most hurt by this are the common investors. People who are just trying to save for retirement or sock away some of their paycheck are discouraged from making solid, stable investments and pushed into more defensive positions – or in the most extreme cases completely away from investing. Those who fall prey to these low-quality analysts end up leaving profits on the table and end up with a smaller stable of savings.
In my opinion, what makes someone worth listening to is whether their thought process makes sense. Pointing to this indicator or that indicator is not enough. Rather, is the analyst presenting a sequence of events that would logically follow? For example, if retail sales are down in a month, but gas prices are down, employment is up and confidence is up, does it make sense that sales are down? No, it is more likely to be an anomaly as all indicators point to Americans having more cash in their pockets. A good analyst will look at those other trends and try to explain the discrepancy; A cheap analyst will declare that a recession is imminent. In this business, it is important to take a holistic approach.
There’s no single summary statistic that gives you the perfect “call” – if it sounds too easy to be true, it probably is. No single data point is a substitute for good decision making, which is the best leading indicator of all.
Neil Dutta is Head of Economics at Renaissance Macro Research.