Ahead of the Curve (28 page)

Lassiter emphasized having people around you who could talk frankly to you in such a way that you would listen. And above all, he said, the coolest thing in the world was to be in love, to be together, and to see your kids grow up. It was a little corny, but it was exactly what I had been longing to hear. It was an affirmation of everything I was feeling. The entrepreneurial life would be hard at times, but there was a way to do it, and it was hardheaded and made a lot of sense. It allowed you to have control over your time and be with the people you loved. This was what I had to come to business school to figure out.

Being free of the section was both a relief and a shock. Ripped from its embrace, we were now exposed to the wolves within our year. It was fascinating to sit in class with new people, to hear new voices, but the mood was less comfortable, more aggressive. In International Financial Management, a small group of Indians who were all returning to jobs in private equity firms formed a clique in the skydeck. They relished challenging Mihir Desai, who always gave it right back, and scorning anything that met their disapproval. In a case involving Nestlé, Desai described visiting the company’s headquarters in Switzerland. To make the point about the difference between American and European corporate cultures, he said that at lunch he had been greeted by a wine waiter who offered two kinds of wine. The skydeck roared with laughter. Oh, the decadence! The disregard for the investor! Those European executives quaffing wine with lunch! While the financiers slapped their desks in glee, I was jotting down a reminder to myself to look into careers with Nestlé.

Max Verlander, my German friend, had alerted me to the anti-Europeanfeelings on campus. He pointed me to a speech given by Jeff Immelt, the chief executive of General Electric, at HBS in 2004. “In the United States, when you go see your customers, customers like you talking about how much money you make,” he said. “It’s a bravado thing. Customers say, hey, that’s great, you make a lot of money. If you go to talk to a customer in Europe about how much money you make, they say, that’s money you stole from me. You’re not supposed to make that much money. You have to alter what you do and how you approach it. There’s nuance. Companies aren’t supposed to make money. It’s Europe.” You bet there’s nuance, I thought.

“It’s all India-China, India-China,” Max said. “People here think Europe is dead.”

The Dynamic Markets class assembled every Monday and Tuesday afternoon to trade stocks, bonds, and their derivatives. Ideally, the course was for people steeped in finance. Graduates of the class joined elite hedge funds or the special situations groups at the top Wall Street banks, where they traded in products the rest of the street barely understood. I was an obvious interloper in this whey-faced and serious group who flicked on their laptops like gunslingers dropping the catch on their pistols. It was such a special class it required two professors rather than the usual one. Josh Coval and Erik Stafford were a curious pair, one small, dark, and restive, the other tall, blond, and artfully laid back, like a Viking at rest. Unlike with every other HBS class, grading was not based on class participation or a final exam but solely on how much money you made trading on a simulated exchange. This was pure. No irritating classmates mouthing off for credit. Just us and the markets. Mano a mano. During the two-hour sessions on Monday, we would trade electronically in a virtual market. On Tuesday, we went through what we had done. Our rankings were posted each week.

In our first class, Stafford strode languidly to the front of his desk and sat down. “Everything we do in this class comes down to one thing,” he said. “The law of one price.” The law of one price states that in an efficient market, all identical goods must have only one price. This seemed blindingly obvious. Imagine a market where ten different vendors sold bananas. The moment one dropped his price, all the others would have to or else they would have no customers. But there would be a fleeting moment for the buyer who spotted the lower price to buy the cheaper bananas and sell them at the higher price before everyone else in the market saw what was going on and dropped their prices. He would be the arbitrageur. Arbitrage is the making of a riskless profit. You sell one product and immediately buy an identical product at a lower price with the funds from the sale. But here in Dynamic Markets, we were not talking about colorful fruit markets populated by cackling, sun-scorched vendors. We were in the world of stone-hearted Wall Street traders for whom a product is a six-digit number flashing green on their screen, amid hundreds of other flashing numbers and fluctuating graphs. To them a product had no color, taste, or texture. It was something that produced cash flows. It could be stock in a company or a bond. It could be the option to buy or sell that stock or bond in several months’ time. What made products identical in the eyes of the trader was if they guaranteed identical cash flows. So you could have a bond that promised to pay you ten dollars in a year and a package of stocks and options, artfully structured, converted into Thai baht via pork belly futures, but if each promised to pay you ten dollars in a year with the same degree of risk, the cost of each of these today should be the same. If they were not, you had an opportunity to make some money.

For the truly red-blooded, there was also risk arbitrage, of which the classic form is merger arbitrage. It involves betting on the likelihood that corporate mergers will occur once they are announced. Company A announces a merger bid for Company B. It is offering a price, X, for Company B, usually above Company B’s current stock market valuation. The merger arbitrageur then sets to work. What, he asks himself, is the likelihood that this deal will actually go through? Will the shareholders of Company B accept it? Will the government block it? Will there turn out to be some hideous accounting fraud at Company B? Will the CEO of Company A fall down an elevator shaft, leaving the company in the hands of its president, who never wanted the merger in the first place? During this period, between the announcement of the merger bid and its completion or collapse, the stock prices of the merging companies move around more than usual as investors weigh up the probability of the merger’s taking place. The skilled merger arbitrageur will assess this probability, judge whether the stock prices are what they should be, and invest accordingly.

While taking this class, I read the former Treasury secretary Robert Rubin’s autobiography. For much of his career, Rubin was a revered risk arbitrageur at Goldman Sachs. He would spend hours sketching out probability calculations on yellow legal pads. It was an approach he brought to government. His entire way of thinking was about understanding risk and reward. When he was Treasury secretary and the Mexican economy imploded, he tried to calculate the scale of the bailout America should offer against the risk of the Mexican economy foundering for years to come. There was no political calculation here. That would be left to others. What Rubin brought to the party was this sense that every risk of every outcome was calculable, and if that was the case, it was possible to limit and prepare for the probability of the worst outcomes occurring. Rubin wrote that the risks most people ignored were the very low probability risks of catastrophic outcomes. They spent an inordinate amount of time worrying about the 20 percent chance of having a bad day and no time thinking about the 1 percent chance of their entire life being turned upside down.

For the risk arb, the 0.1 percent risk of losing $100,000 from your savings is identical to the 20 percent chance of losing your wallet containing $500. The present value cost of both outcomes is $100. Once this way of thinking had burned itself into my brain, it was hard to think any other way. Everywhere I looked in my own life, there seemed to be small risks of total disaster, to which I had never given much thought. Leaving our son with a baby-sitter seemed the most obvious. Perhaps the odds of the baby-sitter turning out to be a kidnapper are 0.01 percent. But what would be the cost of losing him? Infinite. If you thought about this in a Rubin framework, you would only ever entrust your child to someone in whom you had 100 percent confidence.

When people talk about trade-offs in their personal life, they often keep it at a very high level. If I take this job, my salary will be higher, but I’ll spend less time with my family. But what if you turn that “less time with my family” into the actual consequences you most fear? Your spouse will leave you. Your children will treat you as a stranger. And then assign a probability to it. Each outcome used to carry a bearable 5 percent risk. Now that rises to 25 percent. How does that $50,000 pay increase feel now that you have raised the odds of jeopardizing your entire family life by 20 percent?

Sketching out these issues, I found, was more than just a handwriting exercise. It forced me to confront the values I placed on the various parts of my life and how irrational these values were. A small risk of disaster equals a moderate risk of something quite bad. A small chance of great riches is equal to a moderate chance of moderate riches. This was how an arbitrageur saw things, and it made an awful lot of sense. Why didn’t the whole world think like this?

“People do,” Annette told me, “but the reason it doesn’t work is that perceived risk and actual risk are very different. You know, nothing seems so bad until it actually happens. It’s like being in a relationship when you imagine breaking up with the other person, and then when you do, it’s ten times worse than you thought it would be. You have to be really cold to get the probabilities and outcomes right.”

Some of this was basic human psychology. People buy lottery tickets despite the staggering odds against them. As an investment on the riskreward spectrum, it makes no sense. But on the brightening-my-dismal-week-with-the-faint-hope-of-escaping-the-rat-hole-that-is-my-life spectrum, it makes perfect sense. Trying to price out and assess the worst possible scenarios in one’s life, however rational, is no fun at all. So we tend to avoid it. Or overpay for insurance. The profits in the insurance business, after all, arise almost entirely from the difference between the consumer’s and the insurer’s capacities to assess and price risk. I found that trying to apply whatever financialconcepts I actually grasped to nonfinancial situations made understanding Dynamic Markets easier.

During the first half of the course, we were assigned a different partner each week and expected to meet before Monday’s class to practice executing a different trading strategy on the simulated market. One week we had to derive the value of information in supposedly efficient markets; the next, we considered price and liquidity. We used matrix algebra to create portfolios of stocks weighted so as to minimize risk and maximize return. Fortunately, I was paired up twice during this period with Ottavio, a brilliant Brazilian who had worked as a trader before business school and wanted nothing more than to work at a hedge fund afterward. He left me with little to do but praise the trading models he built in Excel and watch while he went at it.

For the second half of the course, however, we were told to pick our own partners. I knew Ottavio would be teaming up with his fellow Brazilian, Rubens. I also knew that if I were to stand even the slightest chance of survival, I needed a hotshot by my side. The moment Coval said, “Pick a partner,” I dashed off an e-mail to Chad, the Section A finance stud. “Chad: You have to be my partner. If not, I am screwed. Completely screwed.” I turned around to see him stuffing his hand into his mouth to stifle a laugh. He looked down and nodded. Across the room, I saw another man from our section banging his fist on his desk. He had e-mailed Chad within seconds of me. But he was too late. I had snared the King.

Each team was given a notional $1 million to manage and told to come up with a name, a prospectus, and a fee structure. Chad and I called ourselves Alchemy and pledged to turn base investments into gold. Another team chose the title “Bringing down the house,” which indeed they did in one class, taking on such a leveraged position that they crashed the entire computer system, forcing Coval and Stafford to redraft the rules of the game. Chad, as I had hoped, created masterly spreadsheets, embedded with options calculators and winking formulae. He tried once or twice to explain how we would be hedging delta or scalping gamma in the upcoming class, but we quickly resolved that I would be his his data entry goon, trading according to the numbers thrown out by his spreadsheets. Computer trading was an eerie sensation. If this had been real, behind each of those numbers blinking on the screen, those moving charts and shifting spreads, would have been communities, factories, and towns. There were mothers dropping off their children at school and going to work, relying on a company to feed their families. But we would just be buying and selling the assets on which these lives depended, as their prices moved by fractions of a decimal point. Trading felt like the furthest possible remove from the workshop and assembly plant, and it was attracting the very brightest minds at HBS because the rewards for doing it well were so outlandish. Yet one had to truly trust in capitalism to believe that trading like this served the goal of efficiently allocating resources in society. Because there was no obvious proof that what you were doing served any purpose besides enriching the trader and his client. One could argue that trading was an effective mechanism for setting prices and that this affected everyone in an economy. But did the traders really deserve the money they made? The rise of the hedge fund investor often felt to me more like a failure of the market rather than a fair allocation of rewards. Sitting in Dynamic Markets, with so many smart classmates focused on these numbers and moving charts, it felt like clever capital was outwitting honest labor, not sharing in an efficient market.

Over the next five weeks, Chad and I marched steadily to the top of the class. Every so often, when Chad’s computer packed in, I would simply eyeball trades for a while, riding up stock or bond prices to make some extra money. This goosed our returns more often than it hurt them. In the final class, we had a chance to vault to the top of the rankings after the Brazilians lost most of their capital on a spread that did not close fast enough. But we never caught up with the leaders, an American man and a German woman. Nonetheless, I walked away with a one from this class. Chad said it was proof that markets could fail. I felt it was my biggest bull run at HBS.