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My Manhattan Project

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I quickly learned how fishy this world could be. A client I knew who specialized in auto loans invited me up to his desk to show me how to structure subprime debt. Eager to please, I promised I could enhance my software to model his deals in less than a month. But when I glanced at the takeout in the deal, I couldn’t believe my eyes. Normally, in a prime-mortgage deal, an investment bank makes only a tiny margin. But this deal had two whole percentage points of juice! Looking at the underlying loans, I was shocked.

“Who’s paying 16 percent for a car loan?” I asked. The current loan rate was then around 8 percent.

“Oh, people who have defaulted on loans in the past. That’s why they’re called subprime,” he informed me. I had known this guy off and on for years. He was an intelligent, articulate, pleasant fellow. He and his wife came to my house for dinner. He had the comfortable manner of someone who had been to good schools—he was not one of the “dudes” trying to jam bonds into a Palm Beach widow’s account. (Those guys were also my clients.)

“But if they defaulted on loans at 8, how can they ever pay back a loan at 16 percent?” I asked.

“It doesn’t matter,” he confided. “As long as they pay for a while. With all that excess spread, we can make a ton. If they pay for three years, they will cure their credit and re-fi at a lower rate.”

That never happened.

In 2001, when my five-year contract expired, Intex let me go. I guess I had become too expensive, and Intex thought they’d be fine without me. Why I had been able to retire at 45 for simply writing a computer program befuddled me and aggravated others who felt they had worked as hard. Life is not always fair, I told them. Right place at the right time. Besides, I explained, the mortgage market is as big, if not bigger, than the stock market. When they screwed up their faces in disbelief, I told them to look around. Every house, every building, every car, plane, boat, and piece of plastic in your wallet has a loan tied to it. It’s all about cash flow.

Within a few months, the World Trade Center was attacked. The country became single-minded in its concerns. As segments of the economy weakened, the American home carried the day. Prices soared, more homes were built, everyone bought granite countertops, new plumbing, new mortgages. Home equity was the piggy bank. It kept Main Street working and Wall Street gorging. By 2003, more than $1 trillion in CMOs were being issued annually.

Banned from Wall Street, I discovered that my summer house, on the North Fork of Long Island, included five acres of underwater land. I applied for permits to grow oysters. I had something to do. In many ways, farming oysters is more difficult, demanding, and frustrating than writing software. Errors take seasons and years to emerge, whereas software is instantaneous. Nature does not give you explicit warning messages; her ways are more subtle and take a lifetime to penetrate. I forgot the day of the week but knew instinctively the tide and the phase of the moon.

Finance, however, is a larger drama. The daily tango of interest rates, money supply, and government debt continued to have an irresistible allure. By 2003, a financial-data firm approached me about writing a structuring tool for collateralized debt obligations, or CDOs. I asked my colleagues, what was a CDO exactly? Like CMOs, they were structured products, but the underlying collateral was not limited to home mortgages. They could be anything—corporate bonds, subprime-mortgage bonds, swaps, or simply air, like the synthetic CDOs: They could be CDOs underwritten by the bonds of other CDOs, CDOs squared. Chicken, pork, offal, chitterlings, tofu salad, fish guts—anything could be run through the grinder. “Diversity of collateral” was the pitch. Some things could go bad, but not everything at once. It never has, except during the Depression, and we’re so much smarter now. That could never happen again.

With prime mortgages, the complexity of the structure is on the bond side: tweaking and fitting hundreds of different bonds from the same bundle of mortgages. But when the underlying collateral is subprime, or the subordinated bonds are supporting several subprime-mortgage deals, then the difficult task is deciding when and if these loans will go under. Default models were the rage. Throw some epsilons and thetas on a paper, hoist a few Ph.D.’s behind your name, and now you’re an expert in divining the future.


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