When Genius Failed

Roger Lowenstein has perhaps captured the most complete and definitive story of hedge fund Long-Term Capital Management. The firm started out as a small group of well pedigreed and incredibly intelligent bond traders at Salomon Brothers and morphed into a private hedge fund that nearly brought the financial system to its knees in the summer of 1998. While the rise of this firm was spectacular the decline was even more so.

The fund was headed by former trader John Meriwether, with partners of the fund being a who’s-who of the financial academic community. Among them were Myron Scholes, Robert C. Merton and former Vice Chairman of the Federal Reserve David W. Mullins. (Merton and Scholes would go on to share the Nobel Prize in 1997 near the peak of LTCM’s performance). The group used mathematical and statistical models to bet on bond spreads–a fairly safe trade most of the time. However, there was a cost to this safety in the form of low rates of return. To improve performance LTCM used leverage or borrowed money to amplify their returns, but this is a catch-22. Leverage not only multiplies returns, but losses as well. As a result small losses can potentially wipe out all of a firm’s equity leaving it insolvent.

Using levered strategies generated four years of spectacular returns for the fund from 1994 through 1997. In 1998 Asian markets began heading south causing LTCM to incur several months of losses, which the firm had thought was near impossible

Indeed, the figures implied that it would take a so-called ten-sigma event–that is, a statistical freak occurring one in every ten to the twenty-fourth power times–for the firm to lose all of its capital within one year. [1a]

By the time August rolled around the firm continued to take losses and was hemorrhaging money at a phenomenal rate. To make matters worse LTCM had over $1 trillion worth of derivative contracts with various Wall Street banks. A default at this point would force the banks to collectively incur the losses of these contracts, potentially leading to much larger systemic problems.

Fearing a massive financial calamity was in the works the Federal Reserve stepped in and helped organize a rescue (read bailout) of LTCM funded by the major banks. The consortium came together at the eleventh hour and collectively infused $3.65 billion into LTCM, thus preventing an impending bankruptcy.

It’s difficult to understand how a group with so much combined intelligence and education could fail to recognize the very real risks that they were taking. One possible explanation comes from the “Winner Effect” as described by John Coates. As the fund continued to generate high returns the stakeholders–partners, banks and investors–continued to plow money into what appeared to be a sure thing. Some went as far as taking out personal loans to reinvest, thus increasing their personal leverage. As success continued so did the risk that the fund and the parties involved were taking. Confidence breeds more confidence, until it all collapses.

Another consideration deals with the application of mathematical models to real world markets. As with any model, those used by LTCM were built on a number of assumptions. Chief among these was the notion of volatility, which the partners took to be synonymous with risk

Now and then, the market might be more volatile, but it will always revert to form–or so the mathematicians in Greenwich believed. It was guided by the unseen law of large numbers, which assured the world of a normal distribution of brown cows and spotted cows and quiet trading days and market crashes. For Long-Term’s professors, with their supreme faith in markets, this was written in stone. It flowed from their Mertonian view of markets as efficient machines that spit out new prices with all the random logic of heat molecules dispersing through a cloud. [1b]

Volatility, as measured by standard deviation, is used to indicate how much prices or returns vary. However, in the world of financial markets, standard deviation fails to capture extreme events that seldom occur, but can be devastating in magnitude. In other words, extreme events occur more often than traditional statistical measures would suggest. Several outsiders, notably Eugene Fama and Paul Samuelson identified this flaw and were cautious about managing real money with LTCM’s models.

The volatility numbers that drove LTCM’s models were based on historic data, which requires a serious warning that every mutual fund investor is familiar with: past performance is no guarantee of future results. The world we live, operate and invest in can change and reverse course at any moment, sometimes without notice. Expecting that future behavior of an asset will resemble its past behavior is a very risky proposition

The belief that tomorrow’s risks can be inferred from yesterday’s prices and volatilities prevails at virtually every investment bank and trading desk. This was Long-Term’s basic mistake, and its stunning losses betrayed the flaw at the very heart–the very brain–of modern finance. [1c]

No matter how smart you are, all of us are subject to bad behavior. Success breeds confidence, and that confidence can, over time, become excessive. The partners of Long-Term Capital Management showed some incredible hubris and recklessness in failing to acknowledge the real possibility of losses and continuing to take on more leverage. No one, no matter how big their IQ, can outsmart the market.

Performance of Long-Term Capital Management

(Before Fees)
(After Fees)
Growth of
Dec 1994 28% 20% $12,000
Dec 1995 59% 43% $17,160
Dec 1996 57% 41% $24,196
Dec 1997 25% 17% $28,309
Oct 1998 -92% $2,300

LTCM Growth of $10k
(Data compiled from Roger Lowenstein’s When Genius Failed)

1. Lowenstein, Roger. When Genius Failed. Random House, Inc. New York, NY. 2000.
(a) p. 127
(b) p. 123
(c) p. 235
2. Testimony of Federal Reserve Board Chairman Alan Greenspan before the Committee on Banking and Financial Services. http://www.federalreserve.gov/boarddocs/testimony/1998/19981001.htm. October 1, 1998.

NOTES – When Genius Failed.pdf

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