The banks also began to turn to inventive devices for moving those deals off of their books. One of these was a type of quasi–shell company known as a structured investment vehicle (SIV) for purchasing the loans and selling the bonds sliced and diced from them. This species had actually first emerged two decades earlier, when two bankers at Citibank, Stephen Partridge-Hicks and Nicholas Sossidis, hit on the idea as a way of getting around the Basel rules for capital requirements. SIVs were tied to banks, not completely separate as with the shell companies known as SPVs. The banks provided some of their funding, as opposed to all of that being raised by the shell company itself, through selling notes. But SIVs did partially fund themselves independently, and they sat off the balance sheets of banks. They were thus a bit like the garage of a house: a useful place for banks to park assets they did not want inside their home banks. Another structure that fulfilled a similar function was a so-called bank conduit. This was similar to a SIV, but more closely linked to a bank.
The hybrid status allowed the banks to evade the Basel rules that limited the amount of assets they could hold on their balance sheets, thereby freeing them to leverage their capital a good deal more. The key reason the banks were allowed to do so concerned the way in which SIVs raised the portion of their funding that didn’t come from the banks, and this particular exploitation of Basel loopholes would lead to terrible consequences once the credit bubble began to burst. The loophole was this: the Basel Accord stated that banks didn’t need to hold capital resources for any credit lines that were less than a year in duration. So banks typically extended credit lines to SIVs and conduits that were 364 days or less. The banks did not reckon, though, that the SIVs and conduits would ever need to draw on these credit lines. In normal circumstances, they raised their funding in the short-term commercial paper market. In this market, the SIVs and conduits would sell notes that paid off in only a few months, somewhat like a CD. Those buying the notes were, therefore, extending credit for many fewer days than a year. The cash they raised was used to purchase safe, long-term debt instruments, such as mortgage bonds. They made a tidy profit because their short-term borrowing costs were lower than the returns they made on the long-term bonds they bought. They were thus playing what’s referred to as a “carry trade,” and while the profit margins on this bit of alchemy were small, the SIVs leveraged themselves so much—making substantial purchases of bonds vis-à-vis the amount of capital they had raised—that all in all they made quite reasonable income. The strategy carried a key risk. Leverage not only magnifies gains, it also magnifies losses, and with such a constant need to replenish short-term funding, the SIVs and conduits were vulnerable to finding themselves cash poor. The danger was that if buyers of commercial paper—such as pension fund managers—ever stopped buying such notes, the SIVs and conduits would see their normal funding dry up. And the SIVs were usually required to continuously report the value of their assets at market prices—to mark-to-market. If those values ever dropped precipitously, commercial paper buyers might well decide to stay away. But the SIVs stuck to buying top-quality assets, only those carrying the triple-A tag from credit rating agencies, so the chances of that turn of events seemed vanishingly slim or so they assumed.
One of the truly staggering things about this boom in newfangled credit investment products was that very few nonbankers had any idea that institutions such as SIVs and CDOs even existed. Even regulators seemed only vaguely aware of what the banks were really doing. Yet SIVs were proliferating like mushrooms after a rainstorm. The financiers had created a vast “shadow banking” system that was running out of control.
As the pace of innovation heated up, credit products were spinning off into a cyberworld that eventually even the financiers struggled to understand. The link between the final product and its underlying assets was becoming so complex that it appeared increasingly tenuous. Bankers were becoming like the inhabitants of the cave in Plato’s tale, who—at best—could see only shadows, not tangible reality. Most financiers lacked the cognitive skills to truly understand the connections in this new world. These complex products could not be analyzed with just a pen and a piece of paper, or even a handheld computer or two. The debt was being sliced and diced so many times that the risk could be calculated only with complex computer models. But most investors had no idea how the banks were crafting their models and didn’t have the mathematical expertise to evaluate them anyway. Each player had its own twist on modeling, after all, and as Terri Duhon observed, “Many different investment banks will provide significantly different prices on the same CDO tranche because they are using different models of correlation.”
Investors generally relied on the ratings agencies to guide them through this strange new land, which seemed a rational, easy solution to contending with the complexity. The ratings scale was so simple: if something was triple-A, it was supposed to almost never default; if it was triple-B or triple-C, it had far more risk. In a world where so much else was baffling, those clear-cut designations were wonderfully comforting. Better still, they were backed up by massive research, which was a key element in the rating agencies’ sales pitch.
Like priests in the medieval church, ratings agency representatives spoke the equivalent of financial Latin, which few in their investor congregation actually understood. Nevertheless, the congregation was comforted by the fact that the priests appeared able to confer guidance and blessings. Such blessings, after all, made the whole system work: the AAA anointment enabled SIVs to raise funds, banks to extend loans, and investors to purchase complex instruments that paid great returns, all without anyone worrying too much.
Some bankers warned about the seduction. “People who are focused on ratings alone are prime fodder for the investment banks to stuff [sell] things too,” argued Charles Pardue, a key player on the team that created BISTRO. “I don’t think we should kid ourselves that everything being sold is fair value. I have been to dealer events where bankers are selling this stuff, and the simplicity of the explanation about how it works scares me…there are people investing in stuff they don’t understand, who really seem to believe the models, and when models change, it will be a very scary thing.”
The ratings agencies, unsurprisingly, were adamant that such concerns were unfounded. Moody’s, Standard & Poor’s, and Fitch had each invested heavily to develop cutting-edge systems for modeling the risks of the full range of new products. To allay fears that their calculations might be faulty, they also had tried to show investors exactly how these systems worked. “We are very transparent in everything we do,” Paul Mazataud, a senior official in the structured finance team of Moody’s, explained in an interview. Moody’s even voluntarily posted details of its own model, called CDOROM, on the internet in 2004. “Our model has become a bit like a template in the market,” Mazataud observed. “Most CDOs are rated with this model, and it is used by management in most synthetic transactions,” he continued, bursting with pride.
Yet such assurances failed to allay the unease of Pardue and others. Precisely because the agencies had diligently posted the details about how their models worked on the net, bankers found it easy to comb through the models looking for loopholes to exploit. And by 2005, they were doing quite a bit of that. Whenever a banker had an idea for a new innovation, it would be run through the agency models to see what rating the product was likely to earn. If it looked too low or high, the design would be tweaked. The aim was to get as high a rating as possible, with the highest level of risk—so that the product could produce all-important higher investor returns. In banking circles, the game was known as “ratings arbitrage.”
Officials at the ratings agencies knew perfectly well that this game was going on. But they felt in a poor position to fight back. Banks had far more resources than the agencies, so they could build better models and hire the smartest structured finance experts. The banks also held the whip hand in a commercial sense. While in the corporate bond world, the agencies rated the bonds of thousand
s of companies and were not dependent on any one company for fees, these credit products were being produced by a much smaller circle of banks. Those banks constantly threatened to boycott the agencies if they failed to produce the wished-for ratings, jeopardizing the sizable fees the agencies earned from the banks for their services. From time to time, the ratings agencies took a stand, to show they couldn’t always be pushed around, but they were careful not to offend the banks too deeply. When an agency gave a rating to a CDO, it sometimes commanded a fee of $100,000 per shot, or even several times that level. Moreover, the business was growing fast—so fast, in fact, that by 2005 Moody’s was drawing almost half of its revenues from the structured finance sector; two decades before, that proportion had been modest.
On top of that conflict of interest, the ratings that the agencies were issuing were subject to another pernicious problem. In trying to judge the risk of these products, the agencies faced the same vexing issue that had dogged the old J.P. Morgan BISTRO team when it considered going into mortgage-based BISTRO deals: How could default patterns be modeled? There was so little good data to work with. Was it safe to assume that defaults would play out in the future as they had in the past? Even if so, the historical data was so limited. The trickiest issue of all was working out the level of “correlation”—figuring out how likely it was that one default would trigger others. Different modelers had alternate ways of dealing with the problem, partly because they often selected different pools of data to work from. “The purest information to use is data on [historic] defaults, but the sample is just too small,” Gareth Levington, a London-based managing director at Moody’s, explained. “So we look at correlations on ratings movements. But there are other ways you can do it with equity prices, say.”
Almost all of these slightly different techniques did, however, use the same fundamental mathematical approach—or “statistical engine,” as Moody’s officials called it—which tried to plot the probability of future defaults based on historical data, using a bell curve type of chart that assumed that losses would occur in a relatively steady manner.
In March 2000, David Li, a researcher at J.P. Morgan, published a groundbreaking paper presenting a method he had devised for estimating the degree of correlation based on a well-established statistical technique known by the formidable name of the Gaussian copula model. This was essentially a way of estimating the degree of dependencies of different kinds among a group of variables. He applied it to CDOs of corporate debt, and his concept quickly spread until almost every bank, ratings agency, and investment group adopted it for their own model. Indeed, some bankers liked to joke that the Gaussian copula was like the combustion engine of the CDO world: enabling them all to craft more and more CDOs faster and faster.
In many ways, Li’s work was a boon for the industry. As the Wall Street Journal pithily pointed out, the launch of Li’s Gaussian copula model meant that bankers had a method not only to weigh a “bag of apples” (i.e., companies) but also to predict the likelihood they would all rot. That gave bankers the ability to trade different pieces of CDO risk with much more confidence—so much so, in fact, that a new business developed called “correlation trading,” which made investment bets based on how the correlation inside a CDO or between CDOs might move. But the model also introduced new risks. The more that banks all relied on Li’s Gaussian copula approach, the more they were creating a new form of correlation risk. Because everyone was using the same statistical method of devising their CDOs to contain risk, in the event of economic conditions that defied that modeling, huge numbers of CDOs would suffer losses all at once. As Alex Veroude, the manager of a CDO for Gulf International Bank, explained, “The problem is that all the structures now are designed the same way, with the same triggers. That means that if there is a storm, all the boats in the water will capsize.”
Worse still, the fundamental philosophy behind the Gaussian statistical technique did not appear to be well suited to cope with a situation where the boats might all capsize, en masse. Like any model, it was only as good as the data that was fed into the “engine,” and that data was usually based on what had happened in the past. If something completely unexpected ever occurred—an event that was not in the data set—the model would not work. Good statisticians tried to avoid that problem by working with as much data as they could. However, the credit world was so new that there was not always that much data available. How could the trajectory of a CDO squared be judged from past data when that “past” was just two years old? Or, for that matter, a subprime-linked derivative that had never been widely traded? How could the models forecast what might happen if a true investor panic got under way, creating a selling chain reaction that had never been seen before? As David Li himself said about the model he had fashioned, “The most dangerous part is when people believe everything coming out of it.”
But even if some observers of the boom were highly skeptical about just how well the risks had really been measured, few had any motive to stop it. No one could argue with the returns. The revenues of the largest investment banks grew 14 percent between 2003 and 2004, to hit $184 billion, producing $61 billion profit. Citigroup, Goldman Sachs, Morgan Stanley, and Deutsche Bank all reported particularly good numbers, with Goldman leading the pack with revenues of more than $16 billion. Some of that was due to the banks’ traditional businesses of equity market underwriting, share trading, and merger advice recovering from the internet crash, but a key component of the growth was the credit boom. Between 2003 and 2004, the total market capitalization of major global banks rose by $900 billion to $5.4 trillion, a record high.
Amid this heady bonanza, however, one bank was notably not celebrating: JPMorgan Chase. By 2004, the bank that had kick-started the credit investment boom was, ironically, lagging badly behind the new pack of players, in large part because the J.P. Morgan management had opted out of the mortgage-based CDO and CDS business. Analysts were unimpressed with the bank’s results, and the stock price was languishing. An injection of new energy was urgently needed, and the bank was about to get quite a shock to the system.
[ SEVEN ]
MR. DIMON TAKES CHARGE
In January 2004, JPMorgan Chase was swept again into the global banking merger mania. This time, the partner was Chicago-based Bank One. On January 14, William Harrison, the CEO of JPMorgan Chase, announced a deal to purchase the bank for $58 billion, one of the biggest deals ever in the financial sector.
The rationale behind the purchase, at least as it was presented by Harrison, was that JPMorgan Chase wanted to expand its retail footprint across the whole of America. Bank One was the sixth largest retail bank in the country and had a formidable network in the Midwest, as well as being the largest single issuer of VISA cards. With 2,300 branches in seventeen states, the JPMorgan Chase–Bank One merged entity would be almost as big as the Citigroup behemoth. The only other close competitor was Bank of America, which was in the process of merging with FleetBoston to create yet another superbank.
Savvy as the deal may have been, some analysts suspected that Harrison was trying to dig himself out of a hole. As the losses from Enron, WorldCom, and other JPMorgan Chase clients had piled up and the bank’s stock price tumbled, he had come under mounting pressure, and Harrison was not a man to go quietly. He needed a way to deflect the criticism and boost the share price, and the Bank One deal did precisely that. When the deal was announced, analysts—as well as the JPMorgan Chase staff—seized on the fact that the merger brought with it the brash financier Jamie Dimon, who had famously helped to build the Citigroup financial empire back in the 1990s.
In early 2004, Dimon was running Bank One, and under the terms of the merger he would technically be junior to Harrison. Dimon was named chief operating officer and president, while Harrison held the post of CEO. But the deal also stipulated that when Harrison, who was sixty, retired in two years, Dimon would take over. Until then, Dimon would be paid 90 percent of whatever Harrison earned, a deal that analyst
s guessed would give Dimon a pay package of around $20 million a year. “Everybody eventually reports to me, but Jamie [Dimon] is the president, COO,” Harrison smoothly explained. “He [Dimon] is running the retail side of the bank, and he’s also running the finance and risk management function…it was a good way to segment responsibilities.”
Wall Street was almost ghoulishly fascinated. The two leaders were near opposites. Dimon was a hard-talking, fast-acting New Yorker—the “boy wonder from Queens,” as Fortune magazine dubbed him; Harrison was described by the same magazine as a “courtly Southern gentleman.” The only thing they clearly shared was a history of frenetically forging bank mergers.
Dimon had built his career by joining with Sandy Weill to gobble up Salomon Brothers, Citibank, and others to forge the Citigroup empire. He also had a long history of ousting the heads of the companies he took over. Inside JPMorgan Chase, the rumor mill speculated about just how long Harrison would survive. “Everybody’s talking about Dimon being the CEO,” a reporter pointed out to Harrison shortly after the deal was announced. “Does it bother you?…Do you feel that in some regard you’re stalled for the next two years?” Harrison blithely batted the question away.
At the time of the merger, Dimon was only forty-seven, a good thirteen years younger than Harrison. But he had already generated more legends than Wall Street financiers twice his age. He grew up in Queens, New York, in a family of Greek immigrants from Smyrna. His grandfather worked as a small-time broker, and Dimon’s father, Theodore, took a similar job on Wall Street, eventually working for Salomon Brothers. As a child, Dimon spent summers working in his father’s and grandfather’s offices. His father’s well-paid job also afforded Dimon a good education. He attended Browning School, a smart prep school on the Upper East Side, then majored in psychology and economics at Tufts University and completed his studies with an MBA from Harvard.
Fool's Gold: How the Bold Dream of a Small Tribe at J.P. Morgan Was Corrupted by Wall Street Greed and Unleashed a Catastrophe Page 12
