Introduction: Excuse Me, Is This the “Real” World?

1

This story goes back a long way, and so do I. In the spring of 1971, I was about to become a newly minted Ph.D. in abstract, or “pure,” mathematics.

I was thinking about what kind of job to get. Almost all the other Ph.D.s in pure mathematics wanted to become professors. That, however, was not my plan. I wanted to apply mathematics, not to teach it. I had always been fascinated by science and technology, and I wanted to be the best at applying mathematics to those fields.

But the Vietnam War raised problems for that plan. In the war’s earlier years, I had organized meetings opposing it. Now that the war was still going full tilt, every scientific or technological firm seemed engaged in the war effort. Nearly all of the big firms and laboratories played a role, researching or manufacturing components for weaponry or defoliants. Jobs that would have challenged and fascinated me were, for me, tainted because they only contributed to a war I didn’t believe in.

Then a fellow student told me he heard that a brokerage firm in Chicago, where I was living, was doing “interesting things with mathematics.”

I interviewed at the firm, A. G. Becker & Company, and was offered a job. I thought, I don’t know anything about the stock market—I don’t even know what it is—but I may as well learn about it. Besides, I should easily be able to get rich using my knowledge of mathematics, and why not? I’m smart; surely I can figure out how to beat the stock market.

Little was I to know how many people I would meet over the years with the same idea, all of whom would be wrong.2

With my new Ph.D. in pure mathematics in hand from Northwestern University, I reported to work at Becker in July 1971. Immediately after starting, my bosses gave me books to read on stock market theories. I was the only mathematician with a Ph.D. in the firm, so I quickly became chief theoretician. I was assigned to work with a young assistant professor at the University of Chicago named Myron Scholes (later to become famous for the Black-Scholes option pricing model), who had been hired as a consultant. I was sent to conferences on quantitative finance, where I rubbed elbows and sat on panels with future Nobel Prize winners.

But within a few short months I realized something was askew. The academic findings were clear and undeniable, but the firm—and the whole industry—paid no real attention to them.

It was as if theoretical physicists knew the laws of thermodynamics, but engineers spent their time trying to construct perpetual motion machines—and were paid very handsomely for it.

The evidence showed that professional investors could not beat market averages. Professional investors couldn’t even predict stock prices better than the nearest taxicab driver.

A study by a young professor named Michael Jensen published in the Journal of Finance in 1968 showed that mutual funds run by professional managers do not beat market averages. Its conclusion said:

The evidence on mutual fund performance discussed above indicates not only that these… mutual funds were on average not able to predict security prices well enough to outperform a buy-the-market-and-hold policy, but also that there is very little evidence that any individual fund was able to do significantly better than that which we expected from mere random chance.1

Academic models showed that highly competitive markets would cause stock prices to change randomly and unpredictably. And many studies similar to Jensen’s have been conducted since then, again and again, overwhelmingly supporting the conclusion.

A. G. Becker, at the time, had the largest database of tax-exempt investment funds in the world. It included pension funds, foundation funds, and endowment funds. There were funds overseen by corporations, state and municipal governments, government agencies, and unions. Some of these funds were enormous, with assets in today’s terms of tens of billions of dollars. Becker’s proprietary database was the largest database of professionally managed funds in existence.3

I had access to this database, and I knew how to program computers. So I used the data to check the academic studies. Sure enough, they were right. The average stock portfolio in our database did not outperform a naive strategy of buying the whole market. Furthermore, the portfolios behaved unpredictably and randomly—there was no way to tell in advance which one would beat the market in any given year.

In spite of this evidence that trying to beat the market was futile, the whole business of the firm—and of the entire industry—was oriented toward trying to beat the market. Sales pitches to sell information (and information, bushels of it, is what Becker sold) always implied that if you have this information, then you’ll be in a better position to beat the market.

The people who did the selling—who were the higher-paid and more impressively titled employees of the firm—did not give a fig for whether it was really possible to beat the market. What they did give a fig for was what would sell the product.

The product, in Becker’s case, was a huge book full of statistics on fund performance that we sold to fund sponsors and fund managers. Believe it or not, this book sold for $20,000 to $30,000—in 1971. It could sell for this price because of the practice of “directed brokerage” or “soft dollars”—of which I will say more in Chapter 2.

Twenty thousand dollars for one book. This sum of money was, at the time, more than enough for four years of tuition at a top-notch college plus room and board. Such was my introduction to the world of incredibly high prices and high levels of compensation.

The huge payoffs brought about a Pavlovian process of sales pitch creation. People found out by trial and error what would work well for selling.

The scientific process creates a hypothesis and tests it against factual reality. The sales process creates a pitch and tests it against market reality to see what sells; factual reality—the truth—is not a necessary consideration.

One salesman I knew could carry on an extended monologue in highly technical-sounding language, punctuating it by repeatedly elbowing his interlocutor in the ribs and poking him in the tie with the wet end of his cigar. What he said made no sense at all, but he sold a lot of Becker books and became the sales manager. (The salespeople were called “consultants,” but they were really only salespeople.)

I quickly realized that the whole industry was about what would sell, and not about what was true or factually based. This was an unaccustomed realization for a mathematician, whose entire course of learning and endeavor was oriented solely toward finding out what is true. Whether a mathematical proof would “sell” is never an issue.4

In short, I was a fish out of water. I did not like the fact that the whole company—and, as far as I could tell, the whole industry—paid little regard to the truth. But I also thought that, perhaps, well, this was business. Academia—especially in cloistered fields like pure mathematics—is not thought of, even by academicians, as the real world. Business is the real world—and here I was. I resolved to try to make the best of it.

Making the best of it means

going against the tide and sneaking the truth into the product while trying not to impair sales;

accepting the language of the business as some sort of code that, though it sounds like a complete distortion of the truth, is really an Orwellian transliteration that everyone in the business understands and interprets correctly; or

succumbing to cynicism, either despising the customers (Michael Lewis in his book Liar’s Poker finally concludes, “The customers were our victims!”) or believing they are so stupid that speaking to them in simplified lies is necessary to help them.

The alternative is to get out of the business. In my subsequent career, I alternated between getting out of the business and staying in it, but trying to go against the tide.

Getting out of the business usually meant accepting a much lower level of compensation. I tried working on renewable energy at a research institution in Colorado for a few years. But this alternative collapsed for me in the oil glut of the early 1980s. So I got back into the investment business.

I became a “lone eagle.” Lone eagle is the Colorado term for an independent consultant who works alone and lives on a mountain-top, communicating with clients electronically and by FedEx. (As time wore on and we were still at it, we were called “bald eagles.”) I consulted to institutional investors on the esoteric mathematics of dynamic asset allocation, risk hedging using options and futures, asset–liability modeling, and portfolio optimization.5

I also authored a computer system to measure investment performance and select money managers. This system was used by a succession of investment firms, from E. F. Hutton to Shearson Lehman to American Express to Smith Barney, and by Dean Witter, Citicorp, and a number of other big firms. Once again, I found myself in possession of a large proprietary database of the performance of investment accounts. Once again, I tested the data to see whether professional managers could beat the market consistently and predictably; and once again, the answer was that they could not.

Each time I got back into the investment field, I tried to leave plenty of time for other activities that I deemed more important—primarily activities in the nonprofit world.

Finally, in the mid-1990s, I became a founding partner and chief economist of a new firm in the investment advisory field, Lock-wood Financial Group. We tried to perform a useful function for the investor and stand by the truth, but our resolve tended to erode in the context of an industry that was already thriving on a lie. In the end, the firm was sold, in September 2002, for a large sum to the Bank of New York—the big New York bank founded by Alexander Hamilton.

Shortly after that, I experienced back-to-back, and at close range, several instances of incredible investment foolishness (which you will soon read about), exhibited by otherwise very smart people. I decided then that it was time to write this book. The message of the book is not new. It has been written many times before—though, it seems, not forcefully enough. If the book is imbued with a sense of outrage, it is because nothing else has worked. The lie perpetrated by the investment world to sell its services at exorbitantly high prices still works all too well.

The lie that it is worth paying a huge amount extra to professional investment service providers to try to beat the market prevails as much today as when I was at A. G. Becker thirty-five years ago. The field has progressed only in finding better and yet more profitable ways to skin clients.

When I occasionally go to a talk on investment theory and practice, I am amazed to find how little things have changed. The talks are still full of the same esoteric but simplistic mathematics. The constructs still begin by blithely assuming, against all the evidence, that many investment professionals have an innate ability to beat the market, that those who do have this innate ability can be identified early enough to benefit from their skills, and that it will be worth the cost.6

This book will try to make crystal clear—through interesting and sometimes humorous experiences and anecdotes, simple explanations of theories, and evidence—what the truth is, what the Big Investment Lie is and how it is sold to us, and what we can do to avoid it. It begins by showing how easy it is to lie—even by accident—and to have that lie accepted, but it takes great marketing and salesmanship to pull it off on a sustained basis. It then shows what the Lie costs us, how it is conveyed using doctored statistics, what the real truth is, how the truth is distorted in the selling process, and how to avoid the Lie and do it right.

Other books have been written on these topics. But this is the first written by a mathematician. It is the first to draw not only on an insider’s knowledge of the industry but also on in-depth mathematical expertise, exposing the Lie’s rotting intellectual foundations. I show that for all the industry’s claims of “sophisticated technology” and “sophisticated mathematics,” its use of these claims to sell its services and justify its charges is absurd, nonsensical, and Swiftian.

For me, this book is a way—at long last—to find a useful application for my experience in the investment field.