How is it possible that stock market bubbles are so obvious after they burst, but are almost never identified in advance – except by what seem, after the fact, to have been a highly perspicacious few? A new study found that there is a way to tell before it bursts that the market, or a segment thereof, is in a bubble. But profiting from an investment strategy designed to exploit bubbles is incredibly difficult.

By Udo J. Keppler (a.k.a. Joseph Keppler, Jr.; 1872-1956), cartoonist [Public domain], via Wikimedia Commons
Take, for example, the tech bubble of 1998-2000. In the 14 months from October 1998 to March 2000, the tech-heavy NASDAQ (as it was typically labelled) climbed 170%. Then, from April to December 2000, it fell 46%. Tech stocks[1], which comprised 18% of total market capitalization at the beginning of the period, rose 128.2% in aggregate then fell 44.6%.

This is a longstanding conundrum that has elicited a variety of stock responses. The broad conventional wisdom is “You can’t time the market.” This forces practitioners of market timing to disguise their strategies under such banners as “tactical asset allocation” or “mean-reversion.”

When is a bubble not a bubble?

Eugene Fama, creator of the concept of three forms of market efficiency, believes that market bubbles don’t exist. In a recent paper by Harvard economists Robin Greenwood, Andrei Shleifer and Yang You, “Bubbles for Fama,” they say that Fama’s argument is, in essence, “that if one looks at stocks or portfolios that have gone up a lot in price, then going forward, returns on average are not unusually low.”

The authors do cite persuasive anecdotal evidence for why Fama would hold his counterintuitive belief. The tech stock experience of 1998-2000 is remembered as a clear-cut case of a bubble because it crashed. But some bubbles keep on bubbling. Greenwood et al. point out that health-sector stocks rose more than 100% from April 1976 to April 1978. But because there was no subsequent crash nobody remembers this as a bubble. These stocks kept going up by more than 65% on average over the next three years and didn’t experience a slump until 1981.

Fama himself challenged the bubble predictions of Robert Shiller, a frequent Fama adversary and one of the perspicacious few credited with having identified the tech bubble in advance. The following is from Fama’s 2013 Nobel Prize lecture:

On the website for his book, Irrational Exuberance, Shiller says that at a December 3, 1996 lunch, he warned Fed chairman Alan Greenspan that the level of stock prices was irrationally high. Greenspan’s famous “Irrational Exuberance” speech followed two days later. How good was Shiller’s forecast? On December 3, 1996 the CRSP index of U.S. stock market wealth stood at 1,518. It more than doubled to 3,191 on September 1, 2000, and then fell. This is the basis for the inference that the original bubble prediction was correct. At its low on March 11, 2003, however, the index, at 1,739, was about 15% above 1518, its value on the initial “bubble” forecast date.

Is there a way to measure whether there really are predictable bubbles?

The Shiller experience shows how difficult it can be to identify a bubble and predict its crash; timing is everything. If you identify a bubble and it bursts 10 years later, it’s not going to count.

Greenwood et al. tried to design a research project to measure whether bubbles, and their demise, can be identified in advance. In doing so, they deployed the commonly-used CRSP database of monthly stock returns since 1926, and SIC industry codes to classify the stocks into industry groups. For international returns they used the Compustat Xpressfeed database and GICS industry sector codes.

The care they took with their methodology should be well-noted:

In analyzing this evidence we have followed particularly simple methodologies. We have not tried to search the data to find ex post optimal screens or combinations of variables to predict returns. … We have not sought to identify dynamic strategies of optimal exit from the industry, but focused on simply exiting after a run-up.

In short, they used simple definitions and did not commit the statistical error of overanalyzing the data. They are very clear about the limitations of their sample size, the possibilities for alternative interpretations of their results, and the reasons why they may not be useful going forward.

Nevertheless, given the severe limitations of statistical analysis of chaotic data – a feature not, as a rule, sufficiently acknowledged in the financial literature – their results are of some interest.

They define the run-up to a potential bubble quite simply: return to an industry group greater than 100% over a two-year period. They identify 40 such incidents in the U.S. stock market since 1926.

They then define a crash of a bubble: more than a 40% drop over the subsequent two-year period.

Bubbles and crashes could, of course, be defined in any number of other ways. But that opens up a Pandora’s box of possible ways to define them, tempting the researcher into a self-defeating data-mining exercise. Better to keep it simple, even though this seem too simple to a “sophisticated” researcher.

Having defined run-ups and crashes in this simple manner, what do they find?

By Michael Edesess, read the full article here.