How Not to Invest

The Barber's Bad Haircut Noah Smith posits that his fellow finance professors are "the world's smartest bad investors." As Smith points out, Nobel laureates Myron Scholes and Robert Merton lack neither brain power nor knowledge of investments, but they flopped spectacularly at Long-Term Capital Management. More recently, MIT's financial-engineering whiz, Andrew Lo, has struggled with his money-management firm. And famously, the brilliant economist Irving Fisher got skinned in the Great Crash of '29.

Smith believes that finance and economics professors struggle because they are too wedded to theory:

Until the last decade or so, theory dominated the literature and empirics took a back seat. In the world of engineering and practical application, that is a recipe for trouble. Theories that have not been rigorously tested against data may get papers published and may win Nobel Prizes, but they will not necessarily work when you try to apply them.

That is doubly true in the financial markets, where there are few permanent principles. Asset prices move not according to the fixed laws of nature, but according to the actions of human beings--market participants, investors, and traders. Human actions may sometimes obey laws, but they often will not, and that is when economic theories will fail.

A better bet, writes Smith, are those investment managers who come from more practically minded fields, such as applied math and computer science. They will be less wedded to their models and more willing to go where the data lead them.

Of that I am not certain. Having spent my earlier years at Morningstar receiving letters from engineers boasting of their "proven" mutual fund trading systems, and my middle years attending business-school classes where Motorola Ph.D.s explained how, by crunching CRSP data, they had figured out how to thrash the stock indexes, I have a healthy respect for the flaws of technically accomplished investors from all backgrounds.

The Quants' Lessons Here are three issues that tend to afflict the quantitatively gifted, from my experience:

1) Overconfidence In a world where the person taking a breakfast order will likely require a calculator to figure out the change, it's easy for the very bright and technically trained to overrate their investment chances. They realize how more much easily numbers come to them than to most, and they know as well the rigors of their educations. Surely, then, they can readily create models that others cannot.

And indeed, they can. The problem is, there's no money to be made by outthinking the masses. Being very good gets a quantitative investor precisely nowhere. If only 1% of the 1% of most numerically talented U.S. adults become investors, that still makes for 20,000 prodigies. Landing in the top 1% of that group would mean something--but such a feat means not only outdoing the rubes, but a whole lot of other Ph.D.s in the process.

Smith relates the tale of Florida finance professor Jay Ritter, who believed early in his career that he had uncovered a glitch in futures prices and could profit by trading on that pattern. Sure enough, Ritter's first few trades succeeded; he attracted some partners; and he upped the stakes. The pattern promptly disappeared. Another smart person--Fischer Black of Goldman Sachs--had gotten in ahead of him.

2) Data mining When I asked the Motorola Ph.D.s why their quantitative stock market models worked, they shrugged. They ran the numbers; obtained the results; measured the success. That was enough for them.

Which, of course, left them in no position to understand why their models had worked in the past and how that might change in the future. Nor did they have a good response if their investment formulas began to fail. In such a case, they could redo the analysis and modify the models--but this would be a form of blind reckoning, because they would be unable to link cause with effect.

While I appreciate Smith's argument that successful investors must know when to let practical considerations overrule theory, and that finance/economics professors may struggle with that balance, surely the reverse also holds true. Those who are adept at manipulating numbers, but who are of a less-theoretical bent, may at times be misled by marketplace noise.

3) Game theory Florida's Ritter failed to appreciate the power of competition. He had succeeded in finding an opportunity, one that avoided the first-level problem of data mining, but he neglected to consider the second-level concern of interactive effects. As Smith writes, the financial markets do not operate by the laws of nature. They are man-made, and morph and adapt according to a different sort of calculus.

While understanding game theory is clearly critical for traders, and others with short-term time horizons who attempt to outguess the competition, it might seem to be beside the point for the truly long-term owner. After all, Warren Buffett invests for several decades, such that his performance is driven mostly by corporate earnings growth, rather than by variations in stock-price multiples. Buffett professes to care not about the vagaries of "Mr. Market."

Yet Buffett famously is an eager and excellent bridge player, who has stated that the game is akin to investing because, "you do whatever the probabilities indicated based on the knowledge that you have at the time, but you are always willing to modify your behavior or your approach as you get new information."

The ability to consider others' actions in forming one's own strategy is critical to succeeding in multiplayer games--and, I suspect, in investing as well.

Conclusion I don't think that finance/economics theorists are as bad as Smith suggests. No doubt, Irving Fisher flubbed the Great Depression, but John Maynard Keynes navigated it (and the subsequent rebound) very well, thanks much. More recently, the senior managements of the fund companies DFA and AQR have not suffered for their Ph.D.s.

But quibbling over the details of the claim is beside the point. The real value of Smith's article is the insight that IQ and market knowledge are necessary but insufficient conditions for investment hopefuls. For full success, investors must be wedded to adaptability, practicality, flexibility--the willingness to modify, when the actions of human beings fail to behave as they did in the past.

Say What? In bemoaning the state of much of academic writing, Smith cites the following passage, from a professor of urban studies:

I conclude then with the invitation to read the urban from the standpoint of absence, absence not as negation or even antonym but as the undecidable. I conclude too with the provocation that theory, including a theory of the urban, can be made from the tealcolored building at the edge of the world that is the Dankuni municipality, a panchayat office repurposed for urban government. But in a gesture befitting the task of provincializing the urban, I note that the dedication plaque for the panchayat building references a fin de siècle poet, Jibanananda Das and his writings on "rupasi bangla," or beautiful Bengal, envisioned as rural and verdant. But Das is also the first urban poet of Bengal, with a set of starkly neo-urban poems that are now etched into the region's self-imagination of urban modernity. The plaque can thus be read as a serendipitous anticipation and premonition of the urban yet to come but its rurality cannot be effaced or erased (Figure 4). The sign of a constitutionally demarcated urban local body, it is the undecidability of the urban.

And you thought investment jargon was bad ... ha!

John Rekenthaler has been researching the fund industry since 1988. He is now a columnist for Morningstar.com and a member of Morningstar's investment research department. John is quick to point out that while Morningstar typically agrees with the views of the Rekenthaler Report, his views are his own.