All Those Bubble Sightings Turned Out to Be Mirages
Calling bubbles is hard. For example, take tech startups. In 2011, billionaire Mark Cuban -- who won much of his own fortune in an earlier tech bubble -- declared that the modern venture-funded startup scene was like a Ponzi scheme. That same year, the Economist warned that irrational exuberance had returned to the tech world, and that investors ought to watch their wallets. Two years later, there had been no bust, but the alarms continued -- Farhad Manjoo, one of my favorite tech writers, warned investors to watch out for the next tech bubble, while Rolfe Winkler and Matt Jarzemsky warned of “froth.” In 2014, Jack Willoughby and Alexander Eule were warning about a new bubble in private-market tech stocks. The torrent of bubble predictions continued into 2015.
QuickTake Watching for Bubbles
Yet the boom persisted for at least five years after the warnings began. Valuations continued to rise, especially for so-called unicorns like Uber Technologies Inc. and other privately owned tech startups worth $1 billion or more. Companies such as Square Inc. and Snap Inc. conducted successful initial public offerings, and venture-capital funding continued to rise. Only very recently have signs of weakness begun to appear -- tech startup funding fell in 2016, high-flying biotech company Theranos Inc. imploded dramatically and Uber’s negative public image may be taking a toll on its valuation. But there still hasn't been anything close to the kind of spectacular bust seen in the early 2000s. As of today, all of the smart people who called a bubble during the past six years are still waiting for their predictions to be borne out.
These examples should demonstrate how hard it is to get the timing of bubbles right -- if it were easy, bubbles probably wouldn’t even exist in the first place, since people wouldn’t buy at inflated prices. So why do people keep calling bubbles over and over?
One reason is that society may give outsized rewards to bubble predictions. Suppose every year for two decades, without reading the news or even looking at stock prices, I issue a newsletter warning darkly of a bubble in the stock market. Then, finally, 20 years after I started issuing my newsletter, there’s a stock market crash. Will I be laughed at for all the years my warnings of doom failed to come true? Probably, by some people. But others will praise my foresight -- after all, I predicted the crash long in advance, didn’t I? The latter group might now hail me as a sage, subscribe to my (suddenly expensive) newsletter and buy my new book.
In other words, the payoff for being right when predicting a rare event might far outweigh the penalty for being wrong. That could lead to a proliferation of opportunistic doomsayers.
But I bet that while there are certainly a few such hucksters around, most bubble predictors are motivated by something very different -- the natural human tendency to find patterns in things that may or may not be random.
There’s plenty of evidence that our life experiences shape our predictions of the future. Economists Ulrike Malmendier and Stefan Nagel have found that people who experienced lower stock market returns early in their lives tend to take less risk. Other economic events, like inflation, also tend to shape future behavior.
Strictly speaking, that’s not rational. There’s no reason why what happened to the stock market when you’re in your 20s is a better guide to the future than what happened a decade before you were born. But people learn from events they live through more than they learn from looking at a historical chart. That means they over-learn some lessons and under-learn others.
This could explain why people who lived through the 1990s tech bubble have been so quick to predict a second one. Even though many things are different this time -- the focus has been on private markets rather than public ones, for example -- the enthusiasm in the sector must feel very familiar. But that familiarity might lead prognosticators astray; the differences might be more important than the similarities.
Yet human reliance on pattern recognition may be one reason bubbles happen in the first place. When the older generation that lived through the last crash is replaced by younger people for whom the 1990s stock market or the 2000s housing bust are ancient history, temporary price rises might generate undue enthusiasm. “This has never happened before!” young investors and asset managers may cry, unaware that it actually has happened many times before.
That suggests that we need the bubble doomsayers, even if they’re often wrong; the wisdom of the scarred older generation may temper the naïve enthusiasm of the youth. Doomsayers also might provide another very important service by disrupting the herd behavior and groupthink that many economists believe exacerbate bubbles. Paradoxically, the more people are out there yelling about bubbles, the less likely investors might be to actually start one.
So we need bubble-callers. But there has to be a balance. If the people giving the warnings start to seem too uninformed or repetitive, they may lose credibility, and people may stop heeding them. That’s why if you’re going to call a bubble, it’s important to bring as much data and insight as possible to the table.
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