Ahead of the Curve Read online

  “I’ve done some ratios,” said the blonde. “Net sales over assets shows Frederick is the better farmer.” The others nodded. But the peasants aren’t selling anything, I thought. They are simply turning their goods over to the feudal landlord. So perhaps feudal tribute over assets might be the better ratio. This wasn’t helpful.

  Next up was “The Case of the Unidentified U.S. Industries.” It was our first foray into finance. From the moment I was accepted by Harvard Business School, I had been dreading finance. I was eager to learn about it, but I worried that I would be so far behind the class technically that everything would sail over my head. That first evening did nothing to boost my confidence. We were given a list of twelve industries, from a basic chemical company and a supermarket chain to a major airline and commercial bank, and an unlabeled set of balance sheet percentages and ratios. We were to match the industry to the correct set of numbers.

  I had gotten the gist of ratios during my summer reading. You compared numbers from financial statements to develop insights into the quality of a business. Take inventory. Companies that need to hold inventory are constantly trying to balance the cost of storing inventory with the need to keep up with supply. It’s like any household. You want enough food to feed the family, but you don’t want so much it’s spilling out of your cupboards and going rotten before you get a chance to eat it. But then again, you might want to buy occasionally in bulk, getting things cheaper, rather than running out every day to the overpriced corner store. Or perhaps you’re a real foodie and like to buy fresh food every day. The point is that different households will have different ways of managing inventory. The only crimes are waste and undersupply. To analyze inventory management in a set of financial statements, you might start with the figures for “cost of goods sold” and “inventory.” “Cost of goods sold,” or COGS, is simply the cost to the manufacturer of the goods it has sold in a given period. “Inventory” is the cost to the manufacturer of the goods it is waiting to sell. Divide COGS by inventory and you get a pretty good idea of how fast the company is shifting product. A ratio of one tells you that the company holds exactly as much inventory as it sells in the period covered by the balance sheet. In a fresh foods market, for a balance sheet covering a year, you would expect an extremely high ratio as inventory is replenished on an almost daily basis. But in a high-end jeweler’s, that ratio might be below one, as each item is held for a long time before it finds a buyer.

  “The supermarket’s going to have the highest inventory turnover,” said Jon.

  “Or the meat packer,” said Jake.

  “The commercial bank will probably have the most current assets and liabilities for deposits and withdrawals,” said the Taiwanese. I could tell Justin was as baffled as I was, from the way he kept tugging at his hair and chin.

  “Wow,” I exclaimed. “I wonder who could make 16.7 percent profit margins. Jewelry stores?” I was just trying to say something.

  Everyone kept scanning the numbers, trying to find meaning. We looked at debt over assets. A company with lots of fixed assets, like factories, would most likely have more debt than an advertising agency, whose main assets were human beings. It is one of the least appealing features of company accounts, and perhaps their greatest flaw, that humans appear only as costs on income statements, never as assets on a balance sheet. Unlike a factory, humans, of course, can get up and walk out the door at any time, hence banks’ reluctance to lend to advertising agencies, law firms, or architectural practices. No chemical plant is going to say to hell with it, default on its loan, and go join an ashram.

  We squinted at net sales over net assets, trying to figure out which companies were generating the most sales from their assets. Again, the ad agency, with nothing but some rented office space and few assets, should have had a high ratio, indicating lots of sales from few assets, whereas the manufacturer would have had a lower one. After an hour, we thought we had nailed down half of them. After two hours, we were up to eight. As the third hour rolled by, it felt as if we would never get there. Just when we thought we had identified the airline, it started to look like the automaker again. Or could it be the maker of name-brand quality men’s apparel?

  I was beginning to feel what would become a familiar set of sensations. The life-sapping effect of fluorescent lighting. The vague stench of Styrofoam and Chinese noodles drifting up from the waste basket. Dehydration and itching skin. The realization that half the people in the room were checking e-mail and surfing the Web, which explained why any question lingered in the air for seconds before stimulating an answer. Through the window, I could see the hulking shadow of Harvard Stadium in the blue-black night. What had begun as a rat-a-tat exchange of thoughts had slowed to dreamlike speed. Words and ideas drifted between us in slow motion. It was nearly midnight when we gave up.

  The air was still hot and thick when I walked out to my car. I drove home to our new apartment in West Cambridge, ten minutes from the business school. There was no one on the streets, and for the first time in a decade I wasn’t living in a major city. My dog, Scarlett, greeted me at the door. She had been waiting patiently on the steps in the dark, and the moment I arrived she burst out to pee on the sidewalk. The lock on the front door was broken. It was unsettling sleeping in an empty apartment in a town I barely knew. My life had been reduced to school and this room with an air mattress on the floor and a picnic table from Costco in the corner. I lay there hearing every single noise, a tree branch scratching against my window, the cars passing outside, their lights shining on the ceiling above me. It took me hours to get to sleep that night as a single question churned around my mind: What have I done?

  We rejoined the battle the next morning at seven. The Spangler meeting rooms were jammed with Math Campers struggling with the as-yet unidentified industries. Their enthusiasm for the task was staggering. The halls rang with discussions of profit margins and leverage ratios. Banks, I heard someone say authoritatively, tended to have huge short-term liabilities—otherwise known as the money in their customers’ accounts, which could be withdrawn at any time—and similarly huge amounts of receivables, or loans made to its customers. For banks, loans are assets, while the money it holds for its customers is a liability. It took me a while to get this straight. The money they have is a liability, whereas the money they have given away is an asset. But once I had figured it out, I looked at my unidentified industries and there it was, leaping out at me, the bank! Finally I had something to offer my group. I raced into the room with my discovery, but they had already figured this out. It was a relief to go to class.

  There are two main classroom buildings at HBS, Aldrich and Hawes, which contain thirty or so almost identical classrooms. Aldrich is named after Senator Nelson Aldrich, a lavishly mustachioed Rhode Islander, whose daughter married John D. Rockefeller, Jr. Rod Hawes graduated from HBS in 1969 and made his fortune in insurance. He built and sold Life Re Corporation of America and has since diverted much of his fortune into philanthropy. In each of the classrooms ninety or so seats ascended in five semicircular rows, divided by two aisles. A few of the rooms had tall windows looking out onto campus, but most had none at all. Sitting in these windowless, temperature-controlled, mercilessly lit rooms was like being in a casino, with no sense of the world outside, immune to time and nature. We were each allotted two laptop widths of space along the curving desks and a swiveling office chair upholstered in purple. When we arrived in class at our assigned seats, we had to slide a white laminated card printed with our names into a slot in front of our place so the professors could identify us. Tucked under each desk were plugs for our computers. To my right was Laurie, an Alaskan with a doctorate in chemistry who previously ran a research center for a biotech company. To my left was Ben, a former employee of the New York City Parks Department. Laurie would spend the two weeks of Math Camp in a state of staring-eyed terror. Despite her obvious brilliance, she dreaded being called on by a professor. Give her a molecule to decompose, she said, she w
ould decompose it, recompose it, and tie it up with a bow. Ask her for an accounting ratio, and she dissolved into a puddle. Ben was much calmer. He wore a beard and sandals and had spent the previous two weeks hiking the Appalachian Trail. Like me, he seemed allergic to his computer and took his notes in longhand. But he evidently had one of those clear, logical minds that would lend itself well to this place. Occupying the lower two thirds of my view was the thick buzz-cut neck of a former marine. For several hours a day, for the next two weeks, his surreal muscles flexed and twitched inches from my face, distracting me from the weighted average cost of capital and decision trees.

  The professors stood in the pit, with a desk for their notes and three sets of blackboards and projector screens to play with. The more adventurous ones could play videos or use a polling gizmo. Students could vote on any issue by pressing one of the buttons built into their desks, red or green, and see the results instantly displayed on a screen up front. The professors could stand close to the front or roam up and down the aisles and rows, spurring their students to talk.

  Harvard Business School had adopted the case method of teaching from the Harvard Law School. Classes begin with a cold call, in which the professor picks out a student to introduce the case we prepared the night before. This can be a harrowing experience for the student, lasting anywhere between two and fifteen minutes. Once the cold call is over, any student can raise his hand to comment. A comment can be a question, a response to something the professor or another student has said, or an example from one’s own experience that clarifies the current problem. The only requirement is that the comment advance the class’s learning.

  Our first professor, David Hawkins, was a bluff Australian who had swum in the Olympics in the early 1950s and still had the broad shoulders and blond hair of a Bondi Beach lifeguard. Arriving in class, he unfolded his newspaper and read out a story from the front page of The Wall Street Journal. It was about a company that had been ordered to restate its earnings because of years of accounting errors. He then rested on the edge of his desk and leaned back, his mouth falling open as he thought. In one hand was a scrunched-up piece of paper, scrawled with his notes for the day’s class. In the other he held a piece of yellow chalk that he would soon be hurling from one blackboard to another to highlight a specific point. “You see,” he said after a moment’s pause, “accounting really does matter. Now, on to this baron.” He hunched his shoulders and began shuffling around the front of the room, dragging one leg, baron-like. “How can it be so hard to tell which of these blasted peasants is the better farmer?” A sense of relief washed over the class. As students were called on to explain their numbers, it became clear that no one had cracked this case. In fact, cracking it was not the point. The purpose of the baron case, Hawkins explained to us, was to demonstrate the difficulty of divining economic truth from even the simplest-seeming situation. In accounting, it was more important to use common sense than to cling to rules.

  During Analytics, the teaching was more casual than what we would face in the RC, but the schedule was identical. Classes began at 8:40 A.M. and each one lasted an hour and twenty minutes. There was a twenty-minute break between the first and second class. On Monday, Wednesday, and Friday there was a third class after lunch, at 1:10 P.M. On Tuesdays and Thursdays we were free at 11:40 A.M. We were then expected to spend a minimum of two hours preparing each case. In addition to accounting, Analytics included crash courses in finance and TOM, that is, Technology and Operations Management. The three subjects were the most mathematical we would be obliged to take during the first year, so we needed to get comfortable with them.

  After Hawkins’s class came finance with Mihir Desai, a young Indian professor, tall and elegant with long, delicate fingers. He ingratiated himself with us immediately by saying that the ideas in finance were simple. It was only the explanations that got complicated. We were not to spend his class staring into our computers tinkering with spreadsheets. Rather, we were to learn finance in such a way that we could explain it to our mothers. Desai promised to come down hard on any Wall Street mumbo jumbo and encouraged us to strip away any preconceptions we might have. Those of us who thought we knew any finance were to relearn it from the bottom up. Those of us who knew nothing were setting out on a great adventure.

  I met up with Justin for lunch. He had grown up in New York, where his father ran a successful investment business. After graduating from college, he had taught in Los Angeles as part of Teach for America and then worked in the New York City mayor’s office. He had come to HBS in large part because the people he most admired in public service had come from successful careers in business. An MBA would be useful whatever he chose to do next. I asked him if he knew what that might be.

  “Not yet,” he said. “I’m going to be looking. If you find anything, tell me.” All around us we heard the same conversation. Where are you from? What did you do? Why did you come to HBS?

  After lunch, we had Technology and Operations Management, taught by Frances Frei, an energetic woman with a boyish thatch of spiky brown hair and a uniform of men’s shirts and dark pants. Our first case with her involved constructing a decision tree, a means of assigning probabilities to the outcomes of certain investment decisions. If I drill for oil in a certain spot, I will have to spend $10 million with two possible outcomes. There is a 30 percent chance I will find nothing and a 70 percent chance I will make a $20-million find. You multiply the percentages by the outcomes to get zero and positive $14 million. So the estimated value of this investment is $14 million minus the $10-million drilling cost, to get $4 million. The usefulness of decision trees depends on the accuracy of your probabilities, but the idea is not to find certainty but to deal more comfortably with uncertainty, to find handholds, however tenuous, in the otherwise sheer rock face of financial decision making.

  In the classes that followed, Frei hustled us on to regression analysis, a means of weighing the importance of different factors on a particular outcome. The case we studied dealt with a bank trying to use customer data to decide what to do about its online services. The bank knew all kinds of things about its customers, from their dates of birth and zip codes to their average balance size and use of online banking. Using Excel, we were required to organize and graph this data to establish behavioral patterns among customers. If they lived close to a branch, were they more likely to visit it? Did their age influence their likelihood of using online services? To what extent? Did customers’ behavior vary by zip code? The bank wanted to use this data to help decide on the size of its investment in further online services, which were cheaper to offer than fully staffed branches. As an Excel virgin, it took me hours to organize thousands of cells of data into neat graphs. But even as I flailed about, I could feel my excitement building at the amount I might learn here. Why did banks send me different mailings from those it sent my neighbor? How did you decide how much money to invest in a project with uncertain outcomes? Having spent most of my life interpreting the world through words and language, it was startling to witness the power of numbers, models, and statistical tools. The full range of my ignorance was becoming apparent, and the prospect of spending two years acquiring an entirely fresh perspective invigorated me.

  On the final day of Math Camp, study groups were pitted against one another in a mock financial negotiation. The subject was the acquisition of a tractor company, and my side was the potential acquirer. We met into the night, preparing our strategy, trying to decide what we could get away with paying. The next day, some groups dressed up in suits to try to assert themselves over their opponents. In our group, the military guys took charge and turned out to be convincing liars and brutal tacticians. We did very well. By the end of Analytics, I was exhausted. I had been working from 7:00 A.M. to midnight every day just to keep up. When Margret, my wife, and Augie, our one-year-old son, arrived, I was delighted to see them. But I was also tired and testy. I had been warned about the HBS bubble, in which even the most trivial tasks assum
ed the most absurd proportions, and it was absolutely true. And this was just the dress rehearsal.

  The first year of the MBA course at Harvard Business School is called the required curriculum, or RC. It is broken down into ten courses, five each semester, intended to cover the fundamentals of business. The first semester courses are finance 1, accounting, marketing, operations, and organizational behavior. The second semester brings finance 2, negotiations, strategy, leadership and corporate accountability, and a macroeconomics course called Business, Government, and International Economics, known to all as “Biggie.” During the second year, the EC, or elective curriculum, we could choose from a wide variety of courses or pursue independent research.

  We would be graded on a forced curve, based on our performance against one another. At the top of the curve would be the academic cream. At the bottom, the stragglers. If everyone gets 95 percent on a test and you get 94 percent, too bad. You will be at the bottom of the curve. In each subject, the top 15 to 25 percent of the class receives a 1, the middle 65 to 75 percent a 2, and the bottom 20 percent a 3. Fifty percent of our grade would be determined by how we participated in class, the quality and frequency of our comments. The remaining 50 percent would be based on our performance in midterms and end-of-term exams. Halfway through each semester, our professors would provide evaluations of our class contributions so we would know how we were doing. After two years, the top 5 percent of the class would be awarded Baker Scholarships, the highest academic honor. The next 15 percent would receive honors. If at any point our academic performance fell below a certain level, we would be warned. Consistently poor performance was known as “hitting the screen,” and would result in suspension or expulsion. If we attended every class, prepared our cases, and spoke in class, this should not be a problem. Except, I wondered, how was I, with no experience of business, supposed to compete academically with students who had studied finance or business at university and then spent several years honing their skills?