The Silo Effect

by Gillian Tett

  As the recriminations flew around, there was a second aspect to the London Whale story, which attracted less attention. This revolved around the question of who was sitting on the other side of JPMorgan’s trade. It is a long-standing adage of markets that trading is a zero sum game: whenever somebody loses money, somebody else wins. Often it is hard to identify the winners and losers, since the gain and pain can be dispersed across the system over many years. But in the case of the trades that CIO’s office had made (which were later dubbed the “whale trades” by bankers and government officials)5 it was possible to identify some winners. One was a $20 billion hedge fund called BlueMountain Capital. In the months before Iksil’s whale trades became public knowledge, traders at BlueMountain had quietly taken the other side of his positions, placing bets in the opposite direction. Initially, they lost money. But when the markets turned in the spring of 2012, BlueMountain’s trade produced a profit, and the fund was later asked to help JPMorgan sell its losing positions. Indeed, the fund eventually earned an estimated $300 million from all its different dealings with JPMorgan over the whale trades.

  When the story about BlueMountain’s gain broke, most observers assumed this was simply a case of a canny hedge fund running rings around a bank, using its expertise to opportunistically chase profits. The staff of BlueMountain Capital included several traders who had once worked at JPMorgan. The cofounder of the fund—an intense, cerebral dark-haired man named Andrew Feldstein—had been part of a team that had created the credit derivatives business at JPMorgan back in the 1990s.6 His traders knew all about the obscure IG9 index and the way that credit derivatives were priced.

  But in reality, there was a second, even more fascinating, aspect to this story that was almost entirely unknown. The reason BlueMountain made its winning trade was not simply that its traders were experts in the technical details of credit derivatives.7 They were also fascinated by silos, or what Feldstein liked to describe as “buckets.” Feldstein had built his hedge fund by studying how big Wall Street banks organized their operations into rigid teams and trading flows, and then trying to find ways to take advantage of the shortcomings that these classification systems created. Indeed, he analyzed the ecosystem with as much intensity as an anthropologist might study kinship groups or religious rituals. However, Feldstein and his colleagues were not driven merely by a spirit of abstract intellectual enquiry. They believed that when rigid silos emerged in the world of finance, they often distorted markets and prices. That created opportunities for canny traders to make profits. Studying classification systems—and silos—was part of their trading strategy. And this often paid off dramatically, as in the case of their anti–whale trades.

  In some senses, this is not surprising. If you dig into the trading strategies of most successful investors, you will often find a tale of boundary-hopping and silo-busting. As John Seely Brown, the scientist and organizational theorist, has observed: “It is at the edge that most innovation occurs and where we can discern patterns that indicate new kinds of opportunities and challenges.”8 When individuals jump across boundaries in business, people are often more creative. Similarly in finance, the richest profits are often found when traders manage to hop between different markets, asset classes, or institutions and question the normal borders. But while the story of BlueMountain is far from unique—and not necessarily the most successful example of this phenomenon—it is interesting because of the bigger lesson it shows. In the first half of the book I described how silos can make people inside institutions behave foolishly. In the previous three chapters I have offered ideas about how companies and institutions can avoid falling prey to some of the problems associated with silos. However, silo-busting is not just a defensive move. It can be offensive too. When people think about the classification systems that their society uses, this can give them a competitive edge compared to rivals. If one institution is hobbled by silos, that can open up opportunities for somebody else. In the case of Sony, a failure to take a coordinated approach toward developing a digital Walkman opened the way for Apple to dominate the market with the iPod. A similar pattern has played out in numerous business spheres. So too with finance. The story of BlueMountain, in other words, is a powerful counterpoint to the tale of how silos damaged UBS, or undermined the prediction of economists at the Bank of England. In that sense, then, it is almost cheering. “We love silos,” Feldstein observed shortly after the dust settled from JPMorgan’s whale trades. “Or at least we love other peoples’ silos. They enable us to make money.”

  THE TALE OF BLUEMOUNTAIN Capital has always been entwined with that of the Wall Street behemoths; the hedge fund, like many of its ilk, operates as the yin to the yang of the big banks. The group was first founded back in 2001, when Feldstein, then thirty-eight, teamed up with a longtime friend and classmate from Harvard Law School, Stephen Siderow, thirty-six, to create a tiny trading operation. Feldstein had spent the previous decade at JPMorgan Bank, where he had been part of a team that helped develop the concept of credit derivatives (or securities that let investors bet whether a loan will turn sour or not) before later rising to a senior management position. Back in the 1990s, as I explained in another book,9 JPMorgan had been a creative place to work, at least to young financiers such as Feldstein. Most employees spent a long period with the bank, and junior bankers often rotated between different departments. That created a slightly more unified culture than was found at many other banks, particularly since some of the leaders of the bank, such as Peter Hancock (who, years later, in 2014, ended up running AIG, the giant insurance group), took pride in using innovative management experiments designed to create a spirit of collaboration and silo-busting.

  However, during the early years of the twenty-first century the atmosphere of the bank changed. In 2000, it merged with another big bank, Chase Manhattan, and became larger and more bureaucratic. This change led to more internal rivalry. And while the pattern was never quite as fragmented or dysfunctional as it was at rivals such as Citigroup or UBS, it frustrated many of the original JPMorgan credit derivatives team, particularly those who had clustered around Hancock.

  In 2001, Feldstein left JPMorgan. He joined forces with Siderow, who had been working as a management consultant at McKinsey, to set up a small hedge fund, tucked into a small, windowless office in Midtown Manhattan. Just like Sony in its early days, when it was founded by entrepreneurs in the bombed-out basement of a department store in Tokyo, or Facebook when Mark Zuckerberg created the group in a cheap, rented Palo Alto house, BlueMountain started out as a flat, freewheeling, informal group. It was easy to swap ideas, and brainstorm financial trades, since everyone was sitting next to everyone else. But Feldstein did not take that unified culture for granted. His years working at JPMorgan had shown him that when financial institutions swelled, they tended to become plagued with bureaucracy and fragmentation. Feldstein believed that caused people to behave in potentially stupid ways, making irrational trades. Or, more accurately, the silos inside big banks created perverse incentives, that often encouraged traders to do things that made sense on a micro level (in terms of the interests of individual desks), but which looked very foolish from a macro-level perspective (or for the bank as a whole).

  When Feldstein talked about this problem, he did not usually express it with the language of social sciences. He had grown up in Arizona, the son of a urologist, and he studied economics as an undergraduate. He later studied law at Harvard, as a classmate of Barack Obama, and excelled in it, since he had a precise, clear mind and good quantitative skills. But along the way, he also acquired an interest in cultural and social systems. When he talked to his colleagues about markets, he tended to see them not just as mathematical models or legal documents, but as cultural patterns that were part of a wider ecosystem. He was fascinated by the social problems created by complexity. So much so that he later became a governor of the Santa Fe Institute, an interdisciplinary group based in New Mexico that studies the science of complex systems
. This was started by physicists but is now run by an anthropologist. Feldstein was particularly interested in how people used classification systems to organize their world—particularly when these taxonomies are flawed. A common theme of anthropological research is that the way we classify the world never really matches the reality of our environments. People might draw neat diagrams of their kinship structures and family trees, but there are often ambiguities, overlaps, and underlaps. Things fall between the cracks. Life does not always fit into the official descriptions of what people are supposed to do. Much of the time we ignore these messy realities. It feels easier to stick with the neat classification systems we have than constantly rewrite them, be that in the sphere of kinship, religion, domestic life, or anything else.

  But Feldstein thought that these messy gaps between rhetoric and reality mattered deeply. When he looked around at the financial system, he could see numerous pieces of the banking sector or markets that did not quite fit into neat boundaries, or the rigid bureaucratic structures of banks. Most bankers tended to ignore those elements, or turn a blind eye. Indeed, financiers generally did not look at financial flows in a particularly wide context. As the tale of UBS in Chapter Three shows, bankers in large organizations are often trained and incentivized to only focus on the bits of finance that sit directly under their noses (and for which they are likely to be paid). But Feldstein, like many savvy hedge fund traders, looked at the entire ecosystem of finance and saw the patterns in a bigger context. He was not only fascinated by the “dancers,” to use the Bourdieu metaphor, but non-dancers too, or the blank spaces on a map that other people did not discuss. He thought these often generated the richest trading opportunities of all.

  One of the first of places where BlueMountain turned this analysis into a strategy was in relation to the type of collateralized debt obligation packages—CDOs—that banks such as UBS started to create at the start of the twenty-first century. At the big banks, as I explained in Chapter Three, these products were typically handled by dedicated desks, or departments. These tended to operate in a very fragmented manner, beset by all manner of different rules. Sometimes the desks at one bank were allowed to deal with entire bundles of loans and derivatives, such as a completed CDO, but were not allowed to trade the individual pieces of that bundle, such as the underlying derivatives, bonds, or loans. Sometimes the desks could only handle the individual derivatives, bonds, or loans, but not trade the entire CDO. Similarly, desks might be allowed to buy assets that carried AAA ratings from the credit rating agencies, but not touch items with other ratings. The patterns varied. It was the financial equivalent of a retail store in which the assistants on one floor were only allowed to sell entire suits, but others could only deal with individual jackets and pants.

  These rigid rules meant that there was uneven demand for different types of financial assets. It was not an open, free, or consistent marketplace. That distorted prices. In addition to that, the different banks each had different, internal models to value the CDOs, or the CDO tranches (as the different pieces of CDOs were known). As a result, the same chunk of credit risk could be given entirely different prices in different corners of the markets, or the silos of the banks. Finance theory suggests that when bankers bundle together diverse packages of bonds and credit derivatives, the price for the entire bundle should reflect the price of the separate constituent parts. The total sum of all the different tranches of a CDO should be similar to the value of the entire CDO. But in reality, because of all these distorted incentives and silos, the price of the entire CDO often diverged from that of the tranches, at least for a period of time.

  So Feldstein’s group tried to analyze the patterns of segmentation in the financial system and the different incentives this created, and then they looked at how this affected prices. Then, in the manner of any trader, they tried to exploit the places where prices seemed to be distorted, by buying low and selling high. Sometimes they used almost childishly simple tactics to do this, buying and selling different pieces of debt to different counterparties. In other cases, though, the tactics were more sophisticated. The BlueMountain team would, for example, sell a few pieces of debt to test the market prices for a particular type of loan, bond, or derivative, and then deliberately create CDOs, or other bundles of credit derivatives, to meet that demand. Then, if they saw that demand was shifting, they would sell other CDO tranches. Either way, as they set about exploiting these price discrepancies, they tended to assume that eventually financial gravity—or economic logic—would win out. Prices for different pieces of credit might be distorted for a period due to the silos. But eventually they tended to revert to the mean, reflecting the fundamental value of the credit risk. The trick, the BlueMountain traders believed, was to buy when debt was distorted—and then pocket the profit when the markets corrected themselves. This was not a remotely glamorous trade. At other hedge funds, traders have sometimes earned great renown by making bold, visible bets on the direction of the economy, a currency, or a company. The hedge fund run by George Soros, for example, bet in the early 1990s that sterling would be devalued, and then made a huge profit when that high-stakes trade turned out to be correct. Bill Ackman’s fund Pershing Square placed a similarly pugnacious bet before the 2007 credit crisis that insurance companies such as MBIA were wrongly valued, which also turned out to be correct. Similarly, John Paulson, another fund manager, created a trading strategy before 2007 that predicted the demise of the subprime mortgage market. However, what BlueMountain Capital was doing was very different from those funds. It was not placing big, glamorous bets on the direction of the economy or a company, but simply making predictions about how the different pieces of credit risk would move relative to each other. Feldstein did not care if markets went up or down. Nor did his traders focus on the chance of a particular company going bankrupt. Or not in isolation. Instead, they looked for discrepancies in value between different securities. It was a technical, often obscure, type of trade. “If there was a mismatch in pricing, we could take advantage of it,” Feldstein explained.I But precisely because it seemed to be so obscure and geeky, the strategy was “uncrowded,” as traders like to say. Or, in plain English, there were relatively few other funds trying to do what BlueMountain did, which made it easier for Feldstein and others to make profits.

  Sometimes Feldstein and his colleagues would wonder why the banks did not try to trade away the price discrepancies themselves. The bankers sitting inside banks were usually bright and many could often see the peculiarities of their own rules. But they were trapped in the system, and the distorted incentives this created. What mattered to the traders was whether the deals they were cutting would boost the profits of their individual team, or silo, not whether it made sense for the entire bank or system. Classic financial theory or orthodox economics did not usually take much account of these micro-level incentives or social structures. Instead, mainstream economists tend to assume that markets are efficient and that mathematical models can explain almost everything. But in reality the complex social structures inside the banks influenced how almost all assets were priced. They even shaped how bankers applied the mathematical models that were supposed to be universal and thus free of any cultural bias.

  And the impact of these social patterns could be surprising. In the first decade of the twenty-first century, for example, Donald MacKenzie, a professor of sociology at Edinburgh University, conducted a study of the financial mathematical models that banks were using to measure the value of their complex instruments. Common sense would suggest that these models should not vary between different banks or asset classes; numbers are numbers across the world. But when MacKenzie did his analysis, he discovered that the way that bankers used the models to measure the value of assets could vary significantly even in similar asset classes. Bankers involved in a corner of the market that traded mortgage-backed CDOs, for example, produced different results with their models than desks that handled asset-backed securities, a related branch of f
inance. This “led to the same instrument or same risk being valued differently,” MacKenzie observed. “In consequence, it [was sometimes] possible to sell the instrument or risk to one market participant while buying it more cheaply from another, with the difference in prices being riskless profit.”10 For BlueMountain that was the sweet spot.

  BY 2009, BLUEMOUNTAIN CAPITAL had grown so fast that it had switched location. It abandoned the tiny, windowless office where it had started and moved into a smart set of offices in an imposing skyscraper on Park Avenue. Fittingly, or perhaps ironically, this location was tucked very close to JPMorgan’s headquarters. It was also near one of the main American offices of UBS. If you looked through the windows on BlueMountain’s trading floor, you could even see the scarlet logo of the Swiss bank on the other side of the street.

  The vista was highly symbolic: a large part of BlueMountain’s strategy was focused on the goal of trying to exploit the mistakes of the big banks such as UBS, particularly in CDOs. Indeed, BlueMounain had become such a powerhouse in the world of credit trading that when Goldman Sachs was forced to list its biggest counterparties in the credit derivatives sector for a Senate investigation, it named BlueMountain as the fourth largest counterparty to its trades in the run-up to the credit crisis, ahead of many big banks.11 “Many credit market participants operate with very broad-based restrictions. That includes anything from insurance company’s ratings regulations to mandates on mutual funds that restrict them to a particular asset duration, geography, credit quality or industry sector,” Siderow told a journalist when he was asked about the fund’s trading strategies.12 “[That means] markets often price the same risk differently depending on the form and the micro story—and these mispricings often take a while to correct.”