The 2018 World Cup and the Cause of the Next Stock Market Crash Lombardi Letter 2018-06-29 11:12:27 2018 world cup FIFA Russia world cup in russia econometrics econometric models steve keen financial collapse mathematical models The continued importance given to financial or econometric modeling will inevitably produce the next major stock market crash as a UBS Group 2018 World Cup prediction goes horribly wrong. Analysis and Predictions 2018,News,Stock Market,Stock Market Crash,U.S. Economy,World Politics

The 2018 World Cup and the Cause of the Next Stock Market Crash

2018 World Cup

World Cup Prediction Exposes Our Vulnerability to the Next Stock Market Crash

Wall Street—not to mention other exchanges—is heading for a major stock market crash.

Indeed, a confluence of factors points to what could be a massive stock market crash. And while there has been no shortage of like-minded opinions on Lombardi Letter, recent developments have turned the possibility of financial collapse into a probability.


I want to discuss one factor in particular: The continued importance given to financial or econometric modeling.

2018 World Cup in Russia

UBS Group AG (NYSE:UBS), one of the world’s top investment banks, where many economists and financial analysts are equipped with the fanciest degrees from prestigious universities, made an interesting prediction in May.

Taking a risk—how apt, since that’s what an investment bank does by definition—it wanted to show off the power and quality of its predictive modeling.

It predicted the team that would end up raising the golden trophy at the 2018 World Cup in Russia.

This is the same kind of modeling or econometrics that “rigorous” financial analysts and bankers use to predict stock performance.

Those models have proven their fallibility. They were as unable to predict the 2008 financial crisis as they were the stock market bubble of 2017–2018, culminating in the Dow Jones’s January record of 26,600 points.

And what did UBS’s econometric modeling and over 10,000 simulations produce?

That Germany, and its famous Mannschaft, no less than the outgoing champions, would repeat the feat of 2014 and be World Champions again. (Source: “Germany Will Win the World Cup, UBS Says After 10,000 Simulations,” Bloomberg, May 17, 2018.)

It turns out, rather, that not only did Germany not win, it did not even make it past the first stage of the competition.

Past Performance and Its Discontents

The key ingredient of the research paper, just as in stock analysis, is past performance.

The central notion is that a company’s past performance—without much regard for present external factors—serves as an infallible indicator of future success.

Certainly, there’s some truth to that. But the excessive reliance on past performance is what has caused a stock market crash. Few things are eternal. The very shape of the Earth itself has changed over the course of millions of years. Climate is changing—not just now, but always.

Thus, reliance on the past can certainly—and importantly—offer some reference points.

Still, especially when converted to statistics and applied in mathematical models—as UBS has done with the World Cup victory predictions—past models fail.

Or, Rather, They Are No More Reliable Than a Roll of the Dice

In 2014, the same UBS model predicted Brazil as the winner of the World Cup. But Germany won it. As for 2018, Germany has already been eliminated from the Group rounds. It has absolutely zero chance of getting a bronze medal, let alone of claiming another cup.

Fans of the other big World Cup favorites—Brazil, Portugal, or Argentina—are crossing their fingers that UBS doesn’t run the model again. They fear their team might come up…and lose at the next opportunity.

UBS—and other investment banks—may have wanted to show off the sophistication and flexibility of their math- and data-based financial models. Unintentionally, they have exposed the very fallacy of those models.

Math Has Poisoned Economics

Physicist Donald Gillies has challenged the very academic and “rigorous” research premises of the economics that inform modern finance. In other words, he has challenged the economics that guides Wall Street analysts.

Those interested may browse one of Gillies’ articles on the subject. But he’s not the only mathematician to question the use of “science” in economics.

Indeed, it’s fair to say that the biggest risk of a stock market crash comes from the over-reliance on math to understand economics.

Such is the opinion of mathematicians and respected thinkers such as former World Bank Economist Paul Romer. (Source: “It’s time to junk the flawed economic models that make the world a dangerous place,” The Guardian, September 19, 2016.)

Steve Keen—you may not have heard of him—was one of few economists to have expected the 2008 financial crash in his book, Debunking Economics. (Source: It’s in all our interests to understand how to stop another Great Depression, The Guardian, October 10, 2011.)

Keen, Romer, Nicholas Nassim Taleb, and other authoritative voices have long complained that economics and finance no longer make sense.

Economic models and econometrics truly—not hyperbolically—use more complicated mathematics than what NASA used to land John and Buzz on the moon in 1969.

Bluntly, Contemporary Economic Theory Is Bunk

When the economy is booming and stocks are flying, everything is great. The economists, Wall Street gurus, and sales desk jockeys congratulate themselves with large bonuses and fly business class to even higher-class vacations.

After a stock market crash, the same analysts pull out models that might as well have been written in Middle Babylonian cuneiform to explain how predictable the financial crisis was—if you knew how to read that cuneiform.

The neo-classical economics that directs the work of top MBA and CFA schools like Harvard or Yale and then trickles down to Wall Street is the stuff that leads to a stock market crash—and a long recession.

The founder of the famous Austrian school of economics described economics as a study of choice and how people use that choice to achieve their material ambitions.

The Austrians were classical economists. They studied factors that range from sociology to psychology to analyze the choices individuals make.

Their successors—the neoclassical types—were envious of the role that physics and other sciences played in shaping the first half of the 20th century. Thus, they wanted to adopt some scientific methodologies or approaches to bestow similar prestige on economics.

Neo-classical economists, despite the wisdom of their “science,” have failed to predict the biggest stock market crash since 1929. They failed to predict October 1987 or the 2000 dotcom bubble. And they have failed to predict the next stock market crash.

The analysts and economics professionals are still abusing economic models. And the UBS 2018 Word Cup winner predictions is an innocent, yet most revealing, example of how wrong the models are.

The Source of Most Market Predictions

Rather than observing the world—in as many of its facets as possible—economists and financial analysts like to trust mathematical models. It’s called “econometrics.”

The more sophisticated the econometrics, the better. That’s why they use special and ever more complex models. They punch in the variables using equally cryptic programs and wait for the calculations.

That’s a crude summary. But the core problem is that the models’ results are presented as being almost infallible. However, economics was never a science.

Economics, the mother of finance, was until the mid-20th century, a branch of history and philosophy. Predictive economics, meanwhile, is more of an art than a science.

Those who are truly gifted at economics are keen observers more than they are keen mathematicians. Though, it does not mean they can’t be both.

Predicting stock performance, therefore, is also more art than science. The belief in the trustworthiness of econometric models—without the infusion of good old-fashioned Socratic doubt—is what ends up causing a massive stock market crash.

Crashes are sudden. They occur because few people have all the necessary information. Even fewer people can synthesize the facts, the sensations, gut feelings, and observations to predict a stock market crash.

Econometrics: Crystal Ball and Alchemy in One Dangerous Package

When a handful—other than insiders—realize what’s happening, they pull out. For the majority, it’s always too late.

Econometric models have been asked—and expected—to harness the observations of the world with numbers, with a bias for the latter. Many of the alarming predictions about climate change, for example, come from self-perpetuating models based on econometrics.

Al Gore said there would be no more Winter Olympics and stated the Arctic would have no more ice—in the summer—by 2013. And those are among the more sensible of the predictions that he quoted scientists as making.

The problem is that the models behave according to previous factors, which is how they were programmed. They can’t predict the future. At best, they reproduce the past.

The financial world of banks, wannabe millionaires, and Nobel prize-aspiring economists uses econometrics as a combination of crystal balls—to predict the outcome—and alchemy—to produce wealth out of paper and thin air.

Ultimately, the failure to remember the basics of philosophy—such as that there’s nothing certain in life—as opposed to pure mathematics is what makes econometric modeling so fallible.

The certainty then leads investors down blind alleys, which always ends in disaster.

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