Hustlers. Cheaters. Crooks. American business has always had them, and sometimes they’ve been punished. But today, those who cheat and put the rest of us at risk are often getting off scot-free. The recent admission of Attorney General Eric Holder that systemically dangerous megabanks may escape prosecution because of their size has opened a new chapter in fraud history. If you know your company won’t be prosecuted, a perverse logic says that you should cheat and make as much money for shareholders as you can.
Jim Chanos is one of America’s best-known short-sellers, famed for his early detection of Enron’s fraudulent practices. In deciding which companies to short (short-sellers make their money when the price of a stock or security goes down), Chanos acts as a kind of financial detective, scrutinizing companies for signs of overvaluation and shady practices that fool outsiders into thinking that they are prospering when they may be on shaky financial footing. Chanos teaches a class at Yale on the history of financial fraud, instructing students in how to look for signs of cheating and criminal activity. I caught up with Chanos in his New York office to ask what’s driving the current era of rampant fraud, who is to blame, what can be done, and the ways in which fraud costs us financially and socially.
Lynn Parramore: You’re often characterized as a short-seller. How does fraud become a concern in that context?
Jim Chanos: One of things we like to say is that in virtually all cases of major financial market fraud over the past 20 years, the only people who really brought forth the fraud into the light were either internal whistleblowers, the press, and/or short-sellers. It was not the normal guardians of the marketplace – regulators, law enforcement, external auditors or people like that — that did it. It was people who had an incentive to come forward either for personal reasons or for profit to point out what was going on at the Enrons and the Sunbeams and Worldcoms. Short-sellers played an important role in the marketplace not only in terms of capping, sometimes, irrational exuberance in terms of prices, but also in ferreting out wrongdoing.
LP: Researchers have created all kinds of tools, like software to detect speech patterns associated with lying, to try to detect fraud. What are some of the best tools for catching financial fraudsters?
JC: There’s no single tool that works all the time, and some of them are kind of interesting, like the voice detection, or Bedford’s law, which looks at numbers and repetition patterns in accounting. But we have seen some models that we work with and I teach in my class– frameworks of fraud and fraud analysis – that have been helpful in looking down through the years where we’ve seen patterns continue. One is a wonderful checklist, the Seven Signs of Ethical Collapse in an organization. Some are clearly intuitive, such as a board full of one’s cronies or an obsession with making earnings forecasts. But some are not so obvious, for example, doing good to mask doing bad.
LP: Good deeds can be a sign of fraud?
JC: One of the more interesting observations in the world of fraud is that some of the most egregious frauds were some of the most philanthropic companies in their communities. In some ways, if you look at Bill Black’s theory of the corporation as both a weapon and shield (we teach a lot of Bill Black’s things in my class), you can begin to see that that would be one way in which the bad guys in corporate suites would basically use the corporation as a shield. They’d say, well, look at all the wonderful things we do in the community, how many people we employ. We give to hospitals, we give to the Little League team, and so on. Not all these things would be immediately obvious to the casual observer.
LP: You’re known for your early detection of Enron’s problems. How does a company like Enron stay in business for years? How is the fraud sustained over time?
JC: It’s one of the great questions, Lynn, and I think that in the case of Enron, there were a lot of people getting rich aiding and abetting what turned out to be to be a fraud. They may not have known it was go-to-jail fraud, as it turned out to be (and most fraud certainly does not end up in jail sentences for the perpetrators, as we know).
But if you look, for example, at the investment banks that were in on structuring the offshore vehicles that Andy Fastow used to offload bad investments from Enron the parent to these vehicles without telling Enron shareholders that he’d also given them a secret agreement that they would made good any losses by issuing Enron stock (that, by the way, was the crux of the fraud of the firm), when you see just how much in fees a lot of the banks and brokers made in these things, there’s an awfully strong incentive to look the other way and not ask the tough questions. That’s really one of the big flaws, I think, in our current market structure.
LP: What do we know about the timing of frauds? When are they most likely to happen?
JC: One of our models is the Kindleberger-Minsky model, named after Hyman Minsky and Charles Kindleberger. It’s a macro model, and basically it takes a look at various market cycles. What we find is that the greatest clustering of fraud in the financial markets occurs, as you might imagine, during and immediately after the biggest bull markets. As I like to tell my students, it’s basically a period in which people suspend their disbelief. Everybody’s getting rich and it becomes increasingly easy to sell more questionable schemes and investments to investors. Typically the major frauds are uncovered or unmasked after the markets decline, for example, Bernie Madoff or Enron, when investors need money from other losses (and often these things have a Ponzi-like nature and can’t finance themselves from a self-sustaining basis) or people simply begin to build back their sense of disbelief and begin to ask tough questions that they didn’t ask during the bull market. So we do see that the fraud cycle generally does track the broader financial market cycles we see with a little bit of a lag.
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