Too Much Counting, Not Enough Trust
Albert Einstein was without peer as a theoretical physicist (save, perhaps, Sir Isaac Newton). Probably no human being in history did more to quantify the seemingly unfathomable mysteries of the universe. But he wasn’t so much into mathematics, saying, “Do not worry about your difficulties in mathematics. I assure you mine are still greater.”
Indeed, Einstein well understood the limits of quantification and the flaws inherent in thinking that counting alone could advance our understanding of how the world works. A sign that hung in his office at the Institute for Advanced Study in Princeton, New Jersey, is as applicable to all other human pursuits as it is to science:
Not everything that counts can be counted, and not everything that can be counted counts.
That rule also applies to the conduct of business affairs. Of course, as the father of relativity, Einstein has to be taken in relative terms. No business can trust everything and count nothing. Nor can any business count everything and trust nothing. It’s all a question of balance, although my own instincts lead me toward far less reliance on counting and far more reliance on trusting. Statistics—in charts, graphs, and tables—can be used to prove almost anything in business, but unquantifiable values have a way of holding steady as a rock.
During my sophomore year at Princeton University, back in 1948, that lesson began to sink into my brain. It was there that my interest in economics began with my study of the first edition of Paul Samuelson’s Economics: An Introductory Analysis. In that ancient era, economics was heavily conceptual and traditional. Our study covered both economic theory and the worldly philosophers from the eighteenth century on—Adam Smith, John Stuart Mill, John Maynard Keynes, and the like. Quantitative analysis was, by today’s standards, conspicuous by its absence. My recollection is that calculus was not even a department prerequisite. (“Quants,” of course—those quantitative strategists who have flooded the financial sector in recent decades and whose track record in the recent market downturn has been so erratic—had not yet entered the field.)
I don’t know whether to credit or blame the original electronic calculator for inaugurating the sea change in the study of how economies and markets work. But with the coming of today’s incredibly powerful personal computers and the onset of the Information Age, numeracy is in the saddle today and rides economics. Einstein’s excellent advice seems to have been largely forgotten. If you can’t count it, it seems, it doesn’t matter.
I disagree with that syllogism. Indeed, I firmly believe that to presume that what cannot be measured is not very important is tantamount to blindness. But before I get to the pitfalls of measurement, to say nothing of trying to measure the immeasurable—things like trust, wisdom, character, ethical values, and the hearts and souls of the human beings who play the central role in all economic activity—I want to discuss the fallacies of some of the popular measurements of the day and the pitfalls created for investors and contemporary society by government, finance, and business.
Today, in our society, in economics, and in finance, we place far too much trust in numbers. Numbers are not reality . At best, they are a pale reflection of reality. At worst, they’re a gross distortion of the truths we seek to measure. But the damage doesn’t stop there. Not only do we rely too heavily on historic economic and market data; our optimistic bias also leads us to misinterpret the data and give them credence that they rarely merit. By worshipping at the altar of numbers and by discounting the immeasurable, we have in effect created a numeric economy that can easily undermine the real one.
Government: Making the Numbers Fit
Many of the numbers that we cannot count on, paradoxically, are produced by our federal government. As Kevin Phillips pointed out in his essay “Numbers Racket,” published in the May 2008 issue of Harper’s
magazine, we’re being grossly misled by government data, including the vital numbers that have become central to our national dialogue, such as our national output or gross domestic product (GDP), our unemployment rate, and our inflation rate.
• It turns out that our GDP includes so-called imputed income, such as the assumed value of income from living in our own homes, the benefits of free checking accounts, and the value of employer-paid insurance premiums. Such phantom income accounts for fully $1.8 trillion (!) of our $14 trillion GDP.
• The Bureau of Labor Statistics proudly reports that our mid-2008 unemployment rate is a relatively low 5.2 percent (albeit up from 5.0 percent earlier in the year). But the number of unemployed excludes workers too discouraged to look for a job, part-time workers looking for full-time jobs, those who want a job but aren’t actively searching for one, and those who are living on Social Security disability benefits. If we include these unemployed souls, the unemployment rate nearly doubles, to 9.0 percent.
• The understatements in the consumer price index (CPI) are even more egregious. Years ago, the cost of living was changed to include “owner-equivalent rent,” which sharply reduced the reported inflation rate during the recent housing boom. The concept of product substitution also was incorporated, meaning essentially that if top-grade hamburger gets too expensive, we substitute a cheaper grade. And (this is really true!) we don’t count cost increases that are attributable to increased quality (“hedonic adjustments”). That is, if airfares double but air travel service is deemed twice as efficient, the calculated cost of air travel is unchanged.
Finance: Attributing Certitude to History
The counting we do in the investment field is also badly flawed. The notion that common stocks are acceptable as investments—rather than merely speculative instruments—can be said to have begun in 1925 with Edgar Lawrence Smith’s Common Stocks as Long-Term Investments. Its most recent incarnation came in 1994, in Jeremy Siegel’s Stocks for the Long Run. Both books unabashedly state the case for equities and, arguably, both helped fuel the great bull markets that ensued. Both, of course, were then followed, perhaps inevitably, by two of the worst bear markets of the past 100 years.
Both books, too, were replete with data, but the seemingly infinite data presented in the Siegel tome, a product of this age of computer-driven numeracy, puts its predecessor to shame. Siegel clearly established that, over two centuries of history, the real return on U.S. stocks has centered around 7 percent per year (about 10 percent in nominal terms, before the erosion of inflation, which has averaged about 3 percent).
But it’s not the panoply of information imparted in Stocks for the Long Run that troubles me. Who can be against knowledge? As Sir Francis Bacon reminded us, “Knowledge is power.” My concern is that too many of us make the implicit assumption that stock market history repeats itself when we know, deep down, that the only valid prism through which to view the market’s future is the one that takes into account not history, but the sources of stock returns, discussed in Chapter 2.
The Experts Are Wrong . . . Again
That experts are so often wrong seems such a self-evident truth that you might well wonder who exactly would be so foolish as to project future returns at past historical rates. Yet the woods are full of expert investor advisers and analysts who do exactly that. Look at the modish popularity of so-called Monte Carlo simulations. The problem with these simulations—essentially calculating the monthly returns on stocks, tossing them into a blender, and casting the seemingly infinite series of permutations and combinations in the form of probabilities—is that by relying simply on historic total returns for their figures, they ignore the sources of those returns.
Yes, speculative returns, which are based on changes in the number of dollars investors are willing to pay for each dollar of corporate earnings—the price-earnings (P/E) ratio—tend to revert to the long-term norm of zero. Yes, corporate earnings growth tends to parallel the nominal growth rate of our economy. (No surprises there!) But no, the contribution of dividend yields to returns depends, not on historic norms, but on the dividend yield that actually exists at the time of the projection of future returns. With the dividend yield at 2.3 percent in July 2008, of what use are historical statistics that reflect a dividend yield that averaged 5 percent—more than twice the present yield? (Answer: None.) Reasonable expectations for future real returns on stocks beginning mid-2008, then, should center around 5 percent, not the historical norm of 7 percent. What could be more elementary than that? But that’s often the trouble with complex calculations: They can’t be trusted to convey simple truths.
Even sophisticated corporate executives and their pension consultants follow the same flawed course. Indeed, one typical corporate annual report expressly stated that “our asset return assumption is derived from a detailed study conducted by our actuaries and our asset management group, and is based on long-term historical returns.” Astonishingly, but naturally, this policy leads corporations to raise their future expectations with each increase in past returns, precisely the opposite of what reason correctly suggests.
At the outset of the bull market in the early 1980s, for example, major corporations assumed a future return on pension assets—bonds as well as stocks—of 7 percent. At the market peak as 2000 began, nearly all firms had sharply raised their assumptions, some to 10 percent or even more. Since pension portfolios are balanced between equities and bonds, they had implicitly raised the expected annual return on the stocks in the portfolio to as much as 15 percent, even as the bear market that followed would make that assumption seem like a bad joke.
If those corporate financial officers had only shut down their computers (and put aside their inherent self-interest in minimizing the contributions to those pension plans) and read John Maynard Keynes instead, they would have known what the numbers were never going to tell them: The bubble created by all the emotions that had fueled the boom—optimism, exuberance, greed, all wrapped in the excitement of the turn of the millennium, the fantastic promise of the Information Age and the New Economy—had to burst. And so, of course, it did, in late March 2000, at the very moment that those rosy 10 percent growth projections were being printed in glossy annual reports.
Clearly, investors would have been wise to set their expectations for future returns on the basis of the current sources of returns, rather than fall into the trap of looking to past returns to set their course. That the dividend yield as 2000 began was at an all-time low of just 1 percent and the P/E at a near record high of 32 times earnings together explain why the average return on stocks in the current decade is at present running at an annual rate of less than 1 percent. If the market remains where it is today at the close of 2009, the decade’s return will be the second lowest of any full decade in history. (In the 1930s, the annual return on the S&P 500 averaged 0.0 percent.)
Business: The Bias toward Optimism
But it’s not just our capital markets that have been corrupted by the perils of relying so heavily on the apparent certitude of numbers. Our businesses, too, have much to answer for, and indeed, the economic consequences of managing corporations by the numbers are both extensive and profound.
The terrible track record of CEOs in predicting growth for their own firms is a well-established fact, but their bias toward optimism—and their use (or, rather, abuse) of numbers to support optimistic assumptions—at least has the excuse of self-interest. Security analysts are supposed to bring a more objective eye to such numbers, but time and again, they too uncritically put on rose-colored glasses and go along for the ride.
With the earnings guidance from the corporations they cover, Wall Street security analysts have, over the past two decades, regularly estimated average future five-year earnings growth. On average, the projections were for growth at an annual rate of 11.5 percent. But as a group, these firms met their earnings targets in only three of the 20 rolling five-year periods that followed. And the actual earnings growth of these corporations has averaged only about one-half of the original projection—just 6 percent.
But how could we be surprised by this gap between guidance and delivery? The aggregate profits of our corporations are closely linked, indeed almost in lock-step, with the growth of our economy. It has been a rare year when corporate profits accounted for less than 4.5 percent of U.S. gross domestic product (GDP), and profits only rarely account for as much as 9 percent. Indeed, since 1929, after-tax profits have grown at an average rate of 5.6 percent annually, actually lagging the 6.6 percent growth rate of the GDP. In a dog-eat-dog capitalistic economy where the competition is vigorous and largely unfettered and where the consumer is king, how could the profits of corporate America possibly grow faster than our GDP?
Our optimistic bias has also led to another serious weakness. In a trend that has attracted too little notice, we have changed the very definition of earnings.While earnings reported to shareholders under generally accepted accounting principles (GAAP) had been the standard since Standard & Poor’s first began to collect the data years ago, in recent years the standard has changed to operating earnings.
Operating earnings, essentially, are reported earnings bereft of all those messy charges like inventory revaluations and capital write-offs, often the result of unwise investments and mergers of earlier years. They are considered nonrecurring, though for corporations as a group they recur, year after year, with remarkable consistency. For the record, reported earnings for the S&P 500 Index over the past decade have averaged $51 per share, while operating earnings averaged $61 per share. The illusory number that we could so easily count was 20 percent higher than the real number that we could actually trust.
What’s more, we now have pro forma earnings—a ghastly formulation that makes new use (or, again, abuse) of a once-respectable term—that report corporate results net of unpleasant developments. Such “no bad stuff” calculations are one more step in the wrong direction. Even auditor-certified earnings have come under doubt, as the number of corporate earnings restatements has soared nearly 18-fold—from 90 in 1997 to 1,577 in 2006. Does that sound like punctilious corporate financial reporting? Hardly. Indeed, it sounds like precisely its opposite.
Loose accounting standards (i.e., loose counting) have made it possible to create, out of thin air, what passes for earnings. One popular method is making an acquisition and then taking giant charges described as nonrecurring, only to be reversed in later years when needed to bolster sagging operating results. But the breakdown in our accounting standards goes far beyond that: ...