False Apostles of Rationality

In April 1998, I traveled from London to the United States to interview several economics and finance professors. It was during this trip that I learned how derivatives had broken down the wall of skepticism between Wall Street and academia. My trip started at the University of Chicago, whose economists had become famous for their theories about market rationality. They argued that markets were supposed to reach equilibrium, which means that everyone makes an informed judgment about the risk associated with different assets, and the market adjusts so that the risk is correctly compensated for by returns. Also, markets are supposed to be efficient—all pertinent information about a security, such as a stock, is already factored into its price. In April 1998, I traveled from London to the United States to interview several economics and finance professors. It was during this trip that I learned how derivatives had broken down the wall of skepticism between Wall Street and academia. My trip started at the University of Chicago, whose economists had become famous for their theories about market rationality. They argued that markets were supposed to reach equilibrium, which means that everyone makes an informed judgment about the risk associated with different assets, and the market adjusts so that the risk is correctly compensated for by returns. Also, markets are supposed to be efficient—all pertinent information about a security, such as a stock, is already factored into its price. (Dunbar 2011, 36-37)

At the university’s Quadrangle Club, I enjoyed a pleasant lunch with Merton Miller, a professor whose work with Franco Modigliani in the 1950s had won him a Nobel Prize for showing that companies could not create value by changing their mix of debt and equity. A key aspect of Miller-Modigliani (as economists call the theory) was that if a change in the debt-equity mix did influence stock prices, traders could build a money machine by buying and shorting (borrowing a stock or bond to sell it and then buying it back later) in order to gain a free lunch. Although the theory was plagued with unrealistic assumptions, the idea that traders might build a mechanism like this was prescient. (Dunbar 2011, 37)

Miller had a profound impact on the current financial world in three ways. He:

  1. Mentored academics who further developed his theoretical mechanism, called arbitrage.
  2. Created the tools that made the mechanism feasible.
  3. Trained many of the people who went to Wall Street and implemented it.

One of the MBA students who studied under Miller in the 1970s was John Meriwether, who went to work for the Wall Street firm Salomon Brothers. By the end of that decade, he had put into practice what Miller only theorized about, creating a trading desk at Salomon specifically aimed at profiting from arbitrage opportunities in the bond markets. Meriwether and his Salomon traders, together with a handful of other market-making firms, used the new futures contracts to find a mattress in securities markets that otherwise would have been too dangerous to trade in. Meanwhile, Miller and other academics associated with the University of Chicago had been advising that city’s long-established futures exchanges on creating new contracts linked to interest rates, stock market indexes, and foreign exchange markets. (Dunbar 2011, 37)

The idea of arbitrage is an old one, dating back to the nineteenth century, when disparities in the price of gold in different cities motivated some speculators (including Nathan Rothschild, founder of the Rothschild financial dynasty) to buy it where it was cheap and then ship it and sell it where it was more expensive. But in the volatile markets of the late 1970s, futures seemed to provide something genuinely different and exciting, bringing together temporally and geographically disparate aspects of buying and selling into bundles of transactions. Buy a basket of stocks reflecting an index, and sell an index future. Buy a Treasury bond, and sell a Treasury bond future. It was only the difference between the fundamental asset (called an underlying asset) and its derivative that mattered, not the statistics or economic theories that supposedly provided a benchmark for market prices. (Dunbar 2011, 38)

In the world Merton Miller lived in, the world of the futures exchanges (he was chairman emeritus of the Chicago Mercantile Exchange when I met him), they knew they needed speculators like Meriwether. Spotting arbitrage opportunities between underlying markets and derivatives enticed the likes of Salomon to come in and trade on that exchange. That provided liquidity to risk-averse people who wanted to use the exchange for hedging purposes. And if markets were efficient—in other words, if people like Meriwether did their job—then the prices of futures contracts should be mathematically related to the underlying asset using “no-arbitrage” principles. (Dunbar 2011, 38)

Bending Reality to Match the Textbook

The next leg of my U.S. trip took me to Boston and Connecticut. There I met two more Nobel-winning finance professors—Robert Merton and Myron Scholes—who took Miller’s idea to its logical conclusion at a hedge fund called Long-Term Capital Management (LTCM). Scholes had benefited directly from Miller’s mentorship as a University of Chicago PhD candidate, while Merton had studied under Paul Samuelson at MIT. What made Merton and Scholes famous (with the late Fischer Black) was their contemporaneous discovery of a formula for pricing options on stocks and other securities. (Dunbar 2011, 38)

Again, the key idea was based on arbitrage, but this time the formula was much more complicated. The premise: A future or forward contract is very similar (although not identical) to the underlying security, which is why one can be used to synthesize exposure to the other. An option contract, on the other hand, is asymmetrical. It lops off the upside or downside of the security’s performance—it is “nonlinear” in mathematical terms. Think about selling options in the same way as manufacturing a product, like a car. How many components do you need? To manufacture a stock option using a single purchase of underlying stock is impossible because the linearity of the latter can’t keep up with the nonlinearity of the former. Finding the answer to the manufacturing problem meant breaking up the lifetime of an option into lots of little bits, in the same way that calculus helps people work out the trajectory of a tennis ball in flight. The difference is that stock prices zigzag in a way that looks random, requiring a special kind of calculus that Merton was particularly good at. The math gave a recipe for smoothly tracking the option by buying and selling varying amounts of the underlying stock over time. Because the replication recipe played catch-up with the moves in the underlying market (Black, Scholes, and Merton didn’t claim to be fortune-tellers), it cost money to execute. In other words you can safely manufacture this nonlinear financial product called an option, but you have to spend a certain amount of money trading in the market in order to do so. But why believe the math? (Dunbar 2011, 38-39)

The breakthrough came next. Imagine that the option factory is up and running and selling its products in the market. By assuming that smart, aggressive traders like Meriwether would snap up any mispriced options and build their own factory to pick them apart again using the mathematical recipe, Black, Scholes, and Merton followed in Miller’s footsteps with a no-arbitrage rule. In other words, you’d better believe the math because, otherwise, traders will use it against you. That was how the famous Black-Scholes formula entered finance. (Dunbar 2011, 39, emphasis added)

When the formula was first published in the Journal of Political Economy in 1973, it was far from obvious that anyone would actually try to use its hedging recipe to extract money from arbitrage, although the Chicago Board Options Exchange (CBOE) did start offering equity option contracts that year. However, there was now an added incentive to play the arbitrage game because Black, Scholes, and Merton had shown that (subject to some assumptions) their formula exorcised the uncertainty in the returns on underlying assets. (Dunbar 2011, 39)

Over the following twenty-five years, the outside world would catch up with the eggheads in the ivory tower. Finance academics who had clustered around Merton at MIT (and elsewhere) moved to Wall Street. Trained to spot and replicate mispriced options across all financial markets, they became trading superstars. By the time Meriwether left Salomon in 1992, its proprietary trading group was bringing in revenues of over $1 billion a year. He set up his own highly lucrative hedge fund, LTCM, which made $5 billion from 1994 to 1997, earning annual returns of over 40 percent. By April 1998, Merton and Scholes were partners at LTCM and making millions of dollars per year, a nice bump from a professor’s salary. (Dunbar 2011, 40)

(….) It is hard to overemphasize the impact of this financial revolution. The neoclassical economic paradigm of equilibrium, efficiency, and rational expectations may have reeled under the weight of unrealistic assumptions and assaults of behavioral economics. But here was the classic “show me the money” riposte. A race of superhumans had emerged at hedge funds and investment banks whose rational self-interest made the theory come true and earned them billions in the process. (Dunbar 2011, 40)

If there was a high priest behind this, it had to be Merton, who in a 1990 speech talked about “blueprints” and “production technologies” that could be used for “synthesizing an otherwise nonexistent derivative security.” He wrote of a “spiral of innovation,” wherein the existence of markets in simpler derivatives would serve as a platform for the invention of new ones. As he saw his prescience validated, Merton would increasingly adopt a utopian tone, arguing that derivatives contracts created by large financial institutions could solve the risk management needs of both families and emerging market nations. To see the spiral in action, consider an over-the-counter derivative offered by investment banks from 2005 onward: an option on the VIX index. If for some reason you were financially exposed to the fear gauge, such a contract would protect you against it. The new option would be dynamically hedged by the bank, using VIX futures, providing liquidity to the CBOE contract. In turn, that would prompt arbitrage between the VIX and the S&P 500 options used to calculate it, ultimately leading to trading in the S&P 500 index itself. (Dunbar 2011, 40-41)

As this example demonstrates, Merton’s spiral was profitable in the sense that every time a new derivative product was created, an attendant retinue of simpler derivatives or underlying securities needed to be traded in order to replicate it. Remember, for market makers, volume normally equates to profit. For the people whose job it was to trade the simpler building blocks—the “flow” derivatives or cash products used to manufacture more complex products—this amounted to a safe opportunity to make money—or in other words, a mattress. In some markets, the replication recipe book would create more volume than the fundamental sources of supply and demand in that market. (Dunbar 2011, 41)

The banks started aggressively recruiting talent that could handle the arcane, complicated mathematical formulas needed to identify and evaluate these financial replication opportunities. Many of these quantitative analysts—quants—were refugees from academic physics. During the 1990s, research in fundamental physics was beset by cutbacks in government funding and a feeling that after the heroic age of unified theories and successful particle experiments, the field was entering a barren period. Wall Street and its remunerative rewards were just too tempting to pass up. Because the real-world uncertainty was supposedly eliminated by replication, quants did not need to make the qualitative judgments required of traditional securities analysts. What they were paid to get right was the industrial problem of derivative production: working out the optimal replication recipe that would pass the no-arbitrage test. Solving these problems was an ample test of PhD-level math skills. (Dunbar 2011, 41)

On the final leg of my trip in April 1998, I went to New York, where I had brunch with Nassim Taleb, an option trader at the French bank Paribas (now part of BNP Paribas). Not yet the fiery, best-selling intellectual he subsequently became (author of 2007’s The Black Swan), Taleb had already attacked VAR in a 1997 magazine interview as “charlatanism,” but he was in no doubt about how options theory had changed the world. “Merton had the premonition,” Taleb said admiringly. “One needs arbitrageurs to make markets efficient, and option markets provide attractive opportunities for replicators. We are indeed lucky . . . the world of finance has agreed to resemble the textbook, in order to operate better.” (Dunbar 2011, 42)

Although Taleb would subsequently change his views about how well the world matched up with Merton’s textbook, the tidal wave of money churned up by derivatives in free market economics carried most people along in its wake.9 People in the regulatory community found it hard to resist this intellectual juggernaut. After all, many of them had studied economics or business, where equilibrium and efficiency were at the heart of the syllabus. Confronted with the evidence of derivatives market efficiency and informational advantages, why should they stand in the way? (Dunbar 2011, 42)

Arrangers as Market Makers

It is easy to view investment banks and other arrangers as mechanics who simply operated the machinery that linked lenders to capital markets. In reality, arrangers orchestrated subprime lending behind the scenes. Drawing on his experience as a former derivatives trader, Frank Partnoy wrote, “The driving force behind the explosion of subprime mortgage lending in the U.S. was neither lenders nor borrowers. It was the arrangers of CDOs. They were the ones supplying the cocaine. The lenders and borrowers were just mice pushing the button.”

Behind the scenes, arrangers were the real ones pulling the strings of subprime lending, but their role received scant attention. One explanation for this omission is that the relationships between arrangers and lenders were opaque and difficult to dissect. Furthermore, many of the lenders who could have “talked” went out of business. On the investment banking side, the threat of personal liability may well have discouraged people from coming forward with information.

The evidence that does exist comes from public documents and the few people who chose to spill the beans. One of these is William Dallas, the founder and former chief executive officer of a lender, Ownit. According to the New York Times, Dallas said that investment banks pressured his firm to make questionable loans for packaging into securities. Merrill Lynch explicitly told Dallas to increase the number of stated-income loans Ownit was producing. The message, Dallas said, was obvious: “You are leaving money on the table—do more [low-doc loans].”

Publicly available documents echo this depiction. An annual report from Fremont General portrayed how Fremont changed its mix of loan products to satisfy demand from Wall Street:

The company [sought] to maximize the premiums on whole loan sales and securitizations by closely monitoring the requirements of the various institutional purchasers, investors and rating agencies, and focusing on originating the types of loans that met their criteria and for which higher premiums were more likely to be realized. (The Subprime Virus: Reckless Credit, Regulatory Failure, and Next Steps by Kathleen C. Engel, Patricia A. McCoy, 2011, 56-57)

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