MarketingMuses

Happy Birthday Golden Gate Bridge!

Today marks the 75^th anniversary of the opening of the Golden Gate Bridge. The San Francisco Bay Area is considered a hotbed of technological innovation and development. Its origin, in this regard, is usually traced to a profusion of post-WWII achievements led by companies such as Hewlett-Packard and Fairchild Semiconductor. However, a strong case can be made that the conception and creation of the Golden Gate Bridge is the true genesis of Bay Area hegemony, for the following five reasons:

It Solved a Real Problem – Before the bridge, ferries transported automobiles between San Francisco and what is now Marin County. A one-way trip took 20 to 30 minutes (depending on departure location), cost $1.00 (twice the initial 50¢ toll for the bridge), and at peak times the wait for a ferry could be several hours.

It is an Early Example of Crowd Sourcing – James Wilkins, a former engineering student, made the bridge proposal that took hold in a 1916 article in the San Francisco Bulletin. San Francisco’s City Engineer estimated the cost at $100 million, and posed an open question to bridge engineers of whether it could be built for less. Joseph Strauss, who had designed several hundred inland drawbridges, responded with initial drawings for two massive cantilevers, one on each side of the strait, connected by a central suspension segment, which he said could be built for $17 million.

It is Innovative Engineering – Joseph Strauss became chief engineer of overall design and construction, but his initial proposal was replaced by a more graceful suspension design from New York’s Manhattan Bridge designer Leon Moisseiff. The basic structural design introduced his “deflection theory,” by which a thin, flexible roadway would flex in the wind, greatly reducing stress by transmitting forces via suspension cables to the bridge towers. When completed, the suspension bridge had the longest main span in the world, at 4,200 feet, and the tallest suspension towers, at 692 feet (above water).

It is Insanely Great Aesthetic Design – Irving Morrow designed the overall shape of the bridge towers, the lighting scheme, and elements such as the streetlights, railing, and walkways. He also decided to keep the vibrant International Orange color, originally used as a sealant for the bridge, instead of the standard silver or gray used on most bridges, because, in addition to its durability, it complements the natural surroundings and enhances the bridge's visibility in fog. In 2007, the Golden Gate Bridge was ranked number five on the American Institute of Architects (AIA) sponsored list of America’s Favorite Architecture.

It is a Symbol of American Persistence – Although the project design, construction, and financing was authorized in 1928, the 1929 crash made it impossible to raise the construction funds as planned. Instead, a $30 million bond measure was passed in 1930. However, there was no buyer for the bonds until Amadeo Giannini, the founder of San Francisco–based Bank of America, agreed on behalf of his bank to buy the entire issue in 1932. Construction began in January 1933 and was completed in April 1937, at a total cost $1.3 million under budget.

Too Big To Control

The $2- to $5-billion loss by the Chief Investment Office of J.P. Morgan Chase is making it difficult for CEO Jamie Dimon to argue that the size of the bank does not contribute to systemic risk.

 

I’ve been writing a series of posts, Risky Business, that look at the U.S. housing market and financial crisis through the lens of risk management. J.P. Morgan Chase is one of the few banks that came through the crisis relatively unscathed in terms of its financial strength and reputation. Much of the credit is given to its brilliant, bold and brazen CEO, Jamie Dimon, and his emphasis on risk management.

In the aftermath of the financial crisis, he has been an outspoken critic of government policies and regulations designed to deal with the hazards of banks deemed too big to fail. He has argued that the size of a financial institution, in and of itself, does not make it more of a systemic risk. Revelations over the last few weeks of a $2- to $5-billion trading loss by the Chief Investment Office—which, supposedly, was responsible for taking charge of the bank's overall risks, and for managing what is now $360 billion of safe, highly liquid securities—is providing ammunition to those that believe size matters, and bigger isn't better.

In today’s Wall Street Journal, Monica Langley provides a revealing look Inside J.P. Morgan’s Blunder. Ms. Langley, author of Tearing Down the Walls: How Sandy Weill Fought His Way to the Top of the Financial World…and Then Nearly Lost It All, has followed Mr. Dimon’s career since his early days as Sandy Weill’s assistant at American Express in the early 1980s. She was able to leverage her relationship and interview him for the article. It’s worth reading the article and watching the video (below) of her discussing the article with Alan Murray.

 

Risky Business (Part 4)

“I agree with most of the commentary that the strong fundamentals support a relatively soft landing in housing.” – Ben Bernanke at his first Federal Reserve Open Market Committee meeting as chairman in March 2006

“We just don’t see troubling signs yet of collateral damage [from the housing price decline] and we are not expecting much.” – Timothy Geithner then president of the Federal Reserve Bank of New York at the Federal Reserve Open Market Committee meeting in September 2006

 

The Big Short: Inside the Doomsday Machine

In early 2006 U.S. housing prices peaked and then started to decline. Increased foreclosure rates over the next couple of years led to a U.S. financial crisis in August 2008. The minutes from 2006 Federal Reserve Open Marketing Committee meetings, released in January 2012, show that the best and brightest economic minds at the Fed were behind the curve in realizing the severity of the downturn and the magnitude of its affect on U.S. financial markets. However, a small group of contrarian investors foresaw the coming crisis.

Michael Lewis previously chronicled his three years in the late 1980s as a bond salesman at Salomon Brothers in his first book, Liar’s Poker.  That experience gives him a keen insight into mortgage-backed securities (during the 1980s, Salomon Brothers was noted for its innovation in the bond market, selling the first mortgage-backed securities).  In The Big Short, he details how a few investors saw through the mass euphoria, purposely-opaque prospectuses, and misleading credit ratings to made bets that would reap billions as the U.S. mortgage derivatives markets collapsed. Michael Lewis excels at character-driven narrative, and I highly recommend the book to anyone who wants to read an interesting, well-written, entertaining story about the complexity of the bond market that contributed to the financial crisis. 

However, the purpose of my writing is to examine risk and risk-management.  The first three posts in this series covered, respectively, the history of risk (Part 1), the evolution of quantitative financial trading (Part 2), and the underestimated role of chance in financial markets (Part 3).  In this post, I simply relay quotes from The Big Short that illustrate how the market for derivatives was perverted to a degree that amplified risk by an order of magnitude not recognized or realized by all but a few investors until after it nearly caused a global financial market collapse.

When Michael Burry bought a credit default swap based on a Long Beach Savings subprime-backed bond [effectively shorting a mortgage-backed security], he enabled Goldman Sachs to create another bond identical to the original in every respect but one: There were no actual home loans or home buyers. Only the gains and losses from the side bet on the bonds were real.

Once they [Goldman Sachs] had this package (a “synthetic CDO,” it was called, which was the term of art for a CDO composed of nothing but credit default swaps), they’d take it over to Moody’s and Standard & Poor’s.  “The ratings agencies didn’t really have their own CDO model,” says one former Goldman CDO trader. “The banks would send their own model to Moody’s and say, ‘How does this look?’”  Somehow, roughly 80 percent of what had been risky triple-B-rated bonds now looked like triple-A-rated bonds.  The other 20 percent, bearing lower credit ratings, generally were more difficult to sell, but they could, incredibly, simply be piled up in yet another heap and reprocessed yet again, into more triple-A bonds.  The machine that turned 100 percent lead into an ore that was now 80 percent gold and 20 percent lead would accept the residual lead and turn 80 percent of that into gold, too.

The pretense that these loans were not all essentially the same, doomed to default en masse the moment house prices stopped rising, had justified the decisions by Moody’s and S&P to bestow triple-A-ratings on roughly 80 percent of every CDO.

It was a stunning opportunity: The market appeared to believe its own lie. It charged a lot less for insurance on a putatively safe A-rated slice of a CDO than it did for the insurance on the openly risky triple-B-rated bonds. Why pay 2 percent a year to bet directly against triple-B-rated bonds when they [investors betting on a decline in the housing market and mortgage defaults by home owners] could pay 0.5 percent a year to make effectively the same bet against the double-A-rated slice of the CDO? If they paid four times less to make what was effectively the same bet against triple-B-rated subprime mortgage bonds, they could afford to make four times more of it.

Financial options were systematically mispriced.  The market often underestimated the likelihood of extreme moves in prices.  The options market also tended to presuppose that the distant future would look more like the present than it usually did. Finally, the price of an option was a function of the volatility of the underlying stock or currency or commodity, and the options market tended to rely on the recent past to determine how volatile a stock or currency or commodity might be.

The market for “synthetics” removed any constraint on the size of risk associated with subprime mortgage lending.  To make a billion-dollar bet, you no longer needed to accumulate a billion dollars’ worth of actual mortgage loans. All you had to do was find someone else in the market willing to take the other side of the bet.

For more than twenty years, the bond market’s complexity had helped the Wall Street bond trader to deceive the Wall Street customer. It was now leading the bond trader to deceive himself.

Even the winning investors in the financial crisis were conflicted in their feelings. “Being short in 2007 and making money from it was fun, because we were short bad guys,” said Steve Eisman [one of the main characters in The Big Short].  “In 2008 it was the entire financial system that was at risk.  We were still short.  But you don’t want the system to crash.  It’s sort of like the flood’s about to happen and you’re Noah.  You’re on the ark.  Yeah, you’re okay.  But you are not happy looking out at the flood. That’s not a happy moment for Noah.”

However, was the financial crisis really the fault of a few bad guys?  That’s the subject of the next post in this series.

 

Risky Business (Part 3)

Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets

“This book is about luck—or, more precisely, about how we perceive and deal with luck in life and business.  Set against the backdrop of the most conspicuous form in which luck is mistaken for skill—the world of [financial] trading.”

 

Nassim Nicholas Taleb is extremely smart, extremely well-read, and has studied deeply and broadly how we humans fool ourselves into believing that being in the right place at the right time is, in fact, proof that we, or others, possess above-average knowledge and skill.  He has been described as an “erudite raconteur,” and this characteristic serves him well in his writing, where he interweaves interesting, entertaining stories of how people fool themselves (or are fooled by others) with rigorous explanations of why this occurs—over and over again.

A primary reason activities and events that involve human behavior cannot be modeled on physical science (see Mandelbrot’s The Variation of Certain Speculative Prices in an earlier post) is that we humans have a built-in bias to attribute our successes to skill, and our failures to bad luck.  Paul E. Meehl demonstrated the “attribution bias” in his 1954 study of experts comparing their perceived abilities to their statistical ones.  “It shows a substantial discrepancy between the objective record of people’s success in prediction tasks and the sincere beliefs of these people about the quality of their performance.”

Taleb is characteristically blunt in his assessment of risk managers in financial institutions.

“Corporations and financial institutions have recently created the strange position of risk manager, someone who is supposed to monitor the institution and verify that it is not to deeply involved in the business of playing Russian roulette.

“The risk managers’ job feels strange: As we said, the generator of reality is not observable.  They are limited in their power to stop profitable traders from taking risks…On the other hand, the occurrence of a blowup [severe trading losses] would cause them to be responsible for it.  What to do in such circumstances?

“Their focus becomes to play politics, cover themselves by issuing vaguely phrased internal memoranda that warn against risk-taking activities yet stop short of completely condemning it, lest they lose their job. (Personal note: this description could also be applied to the public pronouncements of a certain former fed chairman.)

“From the standpoint of an institution, the existence of a risk manager has less to do with actual risk reduction than it has to do with the impression of risk reduction.”

Anyone who has read a mutual fund prospectus is likely familiar with the phrase, “Past performance is no guarantee of future performance.”  However, financial trading institutions routinely make decisions on the basis of past performance.  One routinely used risk management tool is value at risk (VaR), defined as, “a technique used to estimate the probability of portfolio losses based on the statistical analysis of historical price trends and volatilities.”  Taleb is adamant in his opposition to the use of VaR for managing risk.  “An interesting problem is the ‘value at risk’ issue where people imagine that they have a way to understand the risk using ‘complicated mathematics’ and running predictions on rare events—thinking that they were able from past data to observe the probability distributions.  The most interesting behavioral aspect is that those who advocate it do not seem to have tested their past predicting record, another Meehl type of problem.”

So, if the deck is so stacked against 1) accurately assessing the probability of a rare event, 2) including the consequence of a rare event in trading decisions, and 3) avoiding the madness of crowds, how did a small group of traders foresee the financial market collapse and make millions placing contrarian bets?  That’s the subject of the next book. 

 

Paul Solman interviews Nassim Taleb and Benoit Mandelbrot

 

Nassim Taleb urges the U.S. Congress to stop the use of Value at Risk (VaR)

Risky Business (Part 2)

The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It

The book jacket states that, “The Quants is at once a timeless masterpiece of explanatory journalism, a gripping tale of ambition and hubris…and an ominous warning about Wall Street’s future.”  Unfortunately, the book doesn’t quite live up to this characterization, but 50 pages are essential to an understanding of financial risk management. 

 

Scott Patterson, the author and a staff reporter at The Wall Street Journal, opens the book in March 8, 2006 at the Wall Street Poker Night Tournament (now the Math for America Poker Night Tournament).  Gambling is a common interest with many of the quants, and has obvious parallels to the men who developed the science of probability hundreds of years earlier.  The main plot centers on four quants: Peter Muller, head of Morgan Stanley’s PDT hedge fund; Ken Griffin, Manager of Chicago hedge fund Citadel Investment Group; Cliff Asness, founder of AQR Capital Management; and Boaz Weinstein, derivatives trader at Deutsche Bank and head of its Saba internal hedge fund. 

The non-linear narrative shifts between events central to the financial collapse in 2007 and 2008, the back-stories of how the main characters rose from math whizzes to high-powered hedge fund managers, and the role complex mathematics (differential calculus, quantum physics, and advanced geometry), high-speed computing, and the use of immoderate leverage (trading on margin with over 97% borrowed money) played in enabling people who knew little about finance to control billions of dollars in global trading positions.  The story reaches its climax as it describes events in August of 2007, when the billions the hedge funds had gained over a decade of using quantitative techniques were wiped out over the course of a few days.  While this approach makes for a lively story, it is too superficial in its explanation of the role quantitative analysts played in developing the complex structured asset-backed securities (ABS), such as collateralized debt obligations (CDO), that spread risk and uncertainty about the value of the underlying assets more widely, rather than reducing risk through diversification, as promoted. 

50 Pages Well Worth Reading

The fifty pages that comprise chapters two, three, and four are a concise explanation of how quantitative analysis used models from the physical sciences to create a statistical trading advantage, and how strict adherence to these models eventually lead to disaster.  The author uses Ed Thorp, the “Godfather” of the quants, to make what could be a dry, academic subject interesting and engaging. 

As with others who contributed to the science of probability, Ed Thorp initially applied his research to gambling, specifically blackjack.  After reading a ten-page article on blackjack strategy in the Journal of the American Statistical Association by U.S. Army mathematician Roger Baldwin and three colleagues, Thorp contacted Baldwin and obtained the data from the study in 1959.  Thorp was in the process of moving from UCLA, where he had completed his Ph.D. in physics, to an instructor position at MIT.  Once there, he used a university computer (IBM 704) to evolve the blackjack strategy into what would become the basis for his success in casinos and his 1962 bestselling book, Beat the Dealer.  What separated Thorp from those who only focus on probability was his incorporation of a wager strategy that would mitigate his risk of a prolonged losing streak (a statistically wide deviation from the norm) busting him.  He derived his betting strategy from a 1956 paper, A New Interpretation of Information Rate, by John Kelly Jr., a physics researcher at Bell Laboratories.  Using Kelly criterion, he could determine how much to wager, depending on the situation—larger bets when the odds were in his favor and smaller bets when they were not—that maximized return during advantageous periods of play while minimizing the risk of losing everything during periods of disadvantage. 

In 1964, Thorp shifted his sights from blackjack to Wall Street—“the biggest casino of all”—and devised a precise, quantitative method for pricing stock warrants (basically long-term contracts, much like a call option, that investors can convert into common stock).  He combined his pricing formula with a strategy of shorting warrants he judged to be overpriced while going long (buying) an appropriate amount of stock to hedge his bet.  Theoretically, the inverse correlation of the warrant and stock would offset each other, providing the necessary hedge if the warrant price did not fall within the contract period.  In the best-case scenario, the price of the (overpriced) warrant would decline and the stock would rise, closing the inefficiency gap and providing a gain on each side of the trade. 

This strategy, commonly called convertible bond arbitrage (a specific application of delta hedging) became one of the most successful and lucrative trading strategies ever devised.  The only risk was the unlikely event that the price of the warrant would increase while the price of the underlying stock continued to decline.  Thorp understood this risk, and continued to apply Kelly criterion to determine how much he should wager on each trade in order to mitigate the risk of such an unlikely event.  Unlike many hedge funds that focused only on outcome probability, Thorp understood and accounted for the other critical variable in risk management—the potentially devastating consequence of a low probability outcome.

Another person who figures prominently in financial risk management is Benoit Mandelbrot, who showed that unlikely events in financial markets are not that unlikely.  He starts with the dissertation of Louis Bachelier at the University of Paris in 1900 called, “The Theory of Speculation,” that applied Brownian motion from physical sciences to market prices.  Bachelier’s formula showed that the future course of the market is essentially a coin flip.  This discovery came to be called the random walk, and is visually described by a bell curve.  But Mandelbrot observed that the behavior of prices in financial markets (using data from cotton, wheat, railroad stocks, and interest rates that he ran on the supercomputers at IBM, where he worked at the time) frequently demonstrated huge leaps to the outer edges of the bell curve.  In a paper detailing his findings. “The Variation of Certain Speculative Prices,” he asserted that the standard bell curve was not applicable to model financial markets because, “Large price changes are much more frequent than predicted.”  Mandelbrot borrowed a mathematical technique devised by the French mathematician Paul Levy, whom he had studied under in Paris, to measure the erratic behavior of prices.  When plotted on a chart, the curve produced bubbled out on both ends, and became known as “fat tails.” 

Paul Cootner, an MIT finance professor viciously attacked Mandelbrot’s paper in his 1963 book, The Random Character of Stock Market Prices.  He, and other critics, claimed that while Mandelbrot’s model might be accurate for brief time periods, over longer time periods, prices appear to move in a more orderly Brownian fashion.  However, this misses Mandelbrot’s main point that “prices can gyrate wildly over short periods of time—wildly enough to cause massive, potentially crippling losses to investors who’ve made large leveraged bets.”  Mandelbrot’s theories were shelved by the financial engineers who preferred the orderliness of the bell curve and a random walk to the messy, chaotic world of a fat-tail distribution.  But from time to time events such as Black Monday (October 19, 1987), the Long Term Capital Management (LTCM) bailout in September 1998, and the Emergency Economic Stabilization Act of 2008, the mother of all bailouts (at least until several countries in the European Union default on sovereign debt), remind us of the consequences of ignoring unlikely events.

Nassim Nicholas Taleb, a critic of quant models, argues that investors who believe the market moves according to a random walk are “fooled by randomness,” and that models based on historical trends and expectations of a random walk, are bound to lead their users to destruction.  That’s the subject of the next book.