When Genius Failed: The Rise and Fall of Long-Term Capital Management

When Genius Failed: The Rise and Fall of Long-Term Capital Management



The Pequod Review:

When Genius Failed is a book about the 1998 blow-up of a $4 billion hedge fund, but like all great books of its kind, it has much deeper implications — in this case, on the limits of quantitative analysis to predict the future, how tail risks manifest themselves, the blind spots of supposed experts, the speculative nature of finance, and the broader efficiency of markets.

Long-Term Capital Management (LTCM) was started in 1994 by Wall Street veteran John Meriwether, and its investment team included Nobel laureates Myron Scholes and Robert Merton. With its seemingly blue chip collection of talent, it amassed $1 billion of capital from investors. The fund’s strategy would be to use quantitative models to exploit slight deviations in asset prices — typically of government bonds, equity options or interest rate derivatives. LTCM would spot price discrepancies in two nearly-identical instruments, and then go long one asset and short the other asset, with the goal of profiting when the two prices inevitably came back into line. 

Because the price deviations were so small (the markets for these assets were highly efficient, although not perfectly so), LTCM required an enormous amount of leverage in order to generate high equity returns. As of January 1998, when the fund had grown to over $4 billion in size, the company had $124 billion of debt relative to only $4.7 billion of equity — or an enormous 26-to-1 debt-to-equity ratio. This allowed the company to generate high equity returns, but left them exposed if price discrepancies widened (even temporarily) rather than converged (as LTCM expected). 

Of course, that is exactly what happened. Following several years of extraordinary profits (35-40% annual returns) during relatively calm and predictable markets, the 1997-98 financial crises in Asia and Russia caused price spreads on key instruments to widen, leading the fund to incur over $4 billion in losses in just a few months. LTCM may have been able to withstand these hopefully temporary losses except for the fact that their lenders, now fearful that their principal was at risk, began to demand repayment. This forced LTCM to unwind their positions, which caused spreads to widen even further, and led to additional losses. LTCM ultimately required a government-led bailout, which led to an organized sale of the assets and dissolution of the fund.

Roger Lowenstein is a highly intelligent writer with an unusually solid grasp of economics and finance. He describes in persuasive detail LTCM’s business model, its investment thesis, and how the firm’s trades went so disastrously wrong. Readers with a basic understanding of finance will profit more than others, but Lowenstein’s narrative is so good that most general readers will be able to follow the full story. And throughout the book, Lowenstein draws important lessons about financial markets and uncertainty in general:

As the English essayist G. K. Chesterton wrote, life is "a trap for logicians" because it is almost reasonable but not quite; it is usually sensible but occasionally otherwise: "It looks just a little more mathematical and regular than it is; its exactitude is obvious, but its inexactitude is hidden; its wildness lies in wait.”


As Keynes observed, there cannot be "liquidity" for the community as a whole. The mistake is in thinking that markets have a duty to stay liquid or that buyers will always be present to accommodate sellers. The real culprit in 1994 was leverage. If you aren't in debt, you can't go broke and can't be made to sell, in which case "liquidity" is irrelevant. But a leveraged firm may be forced to sell, lest fast-accumulating losses put it out of business. Leverage always gives rise to this same brutal dynamic, and its dangers cannot be stressed too often.


If Wall Street is to learn just one lesson from the Long-Term debacle, it should be that. The next time a Merton proposes an elegant model to manage risks and foretell odds, the next time a computer with a perfect memory of the past is said to quantify risks in the future, investors should run—and quickly—the other way. On Wall Street, though, few lessons remain learned.

This is first-rate economic history.