Christopher Mayer is a commercial loan officer and freelance writer.
Every day at noon a man shows up at a street corner with a green flag and a bugle. Every day he waves the flag and blows a few notes on the bugle. Then he goes away. A police officer notices this man’s behavior and after several days is finally overwhelmed with curiosity. He approaches the man and asks, “What the heck are you doing?” The man replies, “Keeping away the giraffes.” “But there are no giraffes around here,” the officer answers back. “Then I’m doin’ a good job, ain’t I?”
This is an old story that has been told many different ways, but it makes an important point. It shows the logical fallacy of inferring causality from mere proximity. This particular version of the story appeared in Max Gunther’s 1977 book, The Luck Factor. As Gunther notes, “When two events happen simultaneously or consecutively, it may or may not be true that one is the cause of the other.”
In a similar vein, pundits lavish praise and adulation on Chairman Greenspan for allegedly successfully navigating the U.S. economy. They applaud his various maneuverings or offer their own suggestions, as if Greenspan, the central bank, and the U.S. government were all somehow in control, simply throwing levers and pushing buttons on a machine. Here again, just because Alan Greenspan’s Federal Reserve chairmanship has been coincident with the booming economy does not mean he is necessarily in control of it.
Perhaps the credulousness of these observers, their susceptibility to the illusion of control, satisfies some psychological desire to remove uncomfortable uncertainties that seem inherent in a market economy. Unfortunately for them, uncertainty cannot be separated from human existence. “The uncertainty of the future is already implied in the very notion of action,” Ludwig von Mises observed in Human Action.1 To assume that Greenspan or any other government agent can guide the economy down some primrose path is to assume he knows what the future will look like and what the outcome of his actions will be before he takes them.
The Guesswork of Predictions
Many more people might reject the notion that Greenspan should tinker with interest rates and make complicated pronouncements to Congress if they appreciated the impossibility of predicting the future of anything so vast and complicated as the U.S. economy.
Again, Mises understood the folly of this. He wrote, “There is neither constancy nor continuity in the valuations and in the formations of exchange ratios between various commodities. Every new datum brings about a reshuffling of the whole price structure.”2 In the U.S. economy, new data enter constantly and the price structure is always shifting. There are billions of prices, and none of them is constant; nor do they respond in easily predictable ways.
One illustration of the difficulty of prediction is to look at the job analysts have done in predicting the earnings of companies they are paid to follow and study. Investment expert David Dreman studied analyst forecasts in collaboration with Michael Berry of James Madison University.3 The study was subsequently updated to include data to 1996. They took analysts’ quarterly forecasts and compared them to the actual quarterly earnings for the period 1973 through 1996. The forecasts included 94,251 consensus forecasts (each consensus forecast included at least four separate analyst predictions resulting in over 500,000 individual predictions).
The analysts were able to speak with management to help guide them in their own forecasts. They were also able to change their forecasts within three months of quarter-end. These analysts are highly compensated and often educated at the nation’s top schools; their compensation is often tied to their ability to predict.
Despite all these advantages, the study found the average error rate was 44 percent. The error rates also seemed to grow larger over time. Thus despite advances in communications and technology, error rates in the last eight years of the study (from 1996) averaged 50 percent, with two of those years having error rates of 57 and 65 percent.
Dreman eliminated all earnings estimates less than ten cents per share to prevent large percentage errors from distorting the study. (The difference between 3 cents and 4 cents is a whopping 33 percent.) Even after this conservative adjustment, the error rates still averaged 23 percent. This means that, on average, if the consensus forecast called for a dollar in quarterly earnings, the analysts were off by an average of 23 cents. Dreman and Berry further broke down the data and found that the error rates were indistinguishable by industry type. Mature or budding industry, analysts were often wrong by wide margins.
It is astounding that they were so wrong so often.
Now imagine the complexity Greenspan faces in predicting the U.S. economy and determining what the fed funds rate or monetary policy ought to be. Not only does the U.S. economy consist of thousands of individual companies, but they also interact with other countries’ economies. It is truly staggering in complexity.
How likely is it that Greenspan has any clue where the economy is “headed” or what interest rates should be?
Predicting the Past
It is not only difficult to predict the future; often it is also difficult to predict the past. Money manager Murray Stahl published a volume of essays titled Collected Commentaries and Conundrums Regarding Value Investing.4 In the third part of the book, Stahl conducts an interesting experiment.
The start date for his experiment was the summer of 1982. He began by creating a portfolio of six companies that all had major problems: Chrysler, General Public Utilities, Pan American, Massey Ferguson, International Harvester, and White Motor. Chrysler’s problems at the time are well known. General Public Utilities had the problem at the Three Mile Island nuclear plant. Pan American was facing all sorts of difficulties, from intense competition stemming from deregulation to high fuel, interest, and labor costs, and poor management. Massey and International Harvester were agricultural manufacturing companies facing a farm crisis. White Motor was a trucking manufacturer facing similar problems to Chrysler’s. Not having the benefit of a government bailout, White Motor, however, went out of business. Pan American did too.
Stahl asked how such a six-company portfolio would have performed given that two of its holdings became worthless and the other four all faced problems that threatened their very existence. As Stahl notes, “If one had chosen to create on June 30, 1982, an equal weighted portfolio comprised exclusively of these six companies, one’s sanity might well have been questioned.” Even today, knowing ahead of time that two companies would not make it, most investors would not give this portfolio much of a chance.
Surprisingly enough, from June 30, 1982, through December 31, 1993, the portfolio would have returned a compound annual return of 19.2 percent versus only 17.6 percent for the S&P 500 (often used as a benchmark for performance purposes).
Again, even having some knowledge of the market over the period, it is surprising to find that this collection of companies outperformed the market as a whole.
Next time Greenspan solemnly pontificates on the “direction” of the U.S. economy, think about the error rate of Wall Street analysts and think about Stahl’s little experiment. Appreciate the complexity and mass of the U.S. economy and realize the futility of Greenspan’s pronouncements. Greenspan, like the man with the flag and the bugle keeping away giraffes, is every bit the quack for pretending that his maneuverings can guide the economy down some predetermined path.
- Ludwig von Mises, Human Action: A Treatise on Economics (Scholar’s Edition) (Auburn, Ala.: Ludwig von Mises Institute, 1999), p. 105.
- Ibid., p. 118.
- David Dreman, Contrarian Investment Strategies: The Next Generation (New York: Simon & Schuster, 1998), pp. 91–93.
- New York: Horizon Asset Management, 1995.