The turning point of the American Civil War. The defeat of Napoleon. The lead-up to the French Revolution. The decline of Imperial Spain. These chapters of history all have intriguing back stories – according to Harvard professor Niall Ferguson, in his book “The Ascent of Money: A Financial History of the World“.
The back stories, each time, refer to the strengths and weakness of evolving financial systems.
Appreciating these back stories isn’t just an intellectual curiosity. It provides rich context for the view that financial systems are sophisticated and complex entities that deserve much wider understanding. Without this understanding, it’s all too easy for people to hold one or other overly-simplistic understanding of financial systems, such as:
- Financial systems are all fundamentally flawed;
- Financial systems are all fundamentally beneficial;
- There are “sure thing” investments which people can learn about;
- Financial systems should be picked apart – the world would be better off without them;
- Markets are inherently insane;
- Markets are inherently sane;
- Bankers (and their ilk) deserve our scorn;
- Bankers (and their ilk) deserve our deep gratitude.
As the book progresses, Ferguson sweeps forwards and backwards throughout history, gradually building up a fuller picture of evolving financial systems:
- The banking system;
- Government bonds;
- Stock markets;
- Insurance and securities;
- The housing market;
- Hedge funds;
- The growing role of China in financial systems.
Like me, Ferguson was born in Scotland. I was struck by the number of Scots-born heroes and villains the book introduces, including an infamous Glaswegian loan shark, the creators of the first true insurance company, officers of the companies involved in the Anglo-China “Opium Wars”, and Andrew Law – instigator in France of one of history’s first great stock market bubbles. Of course, many non-Scots have starring roles too – including Shakespeare’s Shylock, the Medicis, the Rothschilds, George Soros, the managers of Enron, Milton Friedman, and John Maynard Keynes.
Time and again, Ferguson highlights lessons for the present day. Yes, new financial systems can liberate great amounts of creativity. Innovation in financial systems can provide significant benefits for society. But, at the same time, financial systems can be mis-managed, with dreadful consequences. One major contributory cause of mis-managing these systems is when people lack a proper historical perspective – for example, when the experience of leading financiers is just of times of growth, rather than times of savage decline.
Among many fascinating episodes covered in the book, I found two to be particularly chilling:
- The astonishing (in retrospect) over-confidence of observers in the period leading up to the First World War, that any such war could not possibly happen;
- The astonishing (in retrospect) over-confidence of the managers of the Long Term Capital Management (LTCM) hedge fund, that their fund could not possibly fail.
Veteran journalist Hamish McRae describes some of the pre-WWI thinking in his review of Ferguson’s book in The Independent:
The 19th-century globalisation ended with the catastrophe of the First World War. It is really scary to realise how unaware people were of the fragility of those times. In 1910, the British journalist Norman Angell published The Great Illusion, in which he argued that war between the great powers had become an economic impossibility because of “the delicate interdependence of international finance”.
In spring 1914 an international commission reported on the Balkan Wars of 1912-13. The British member of the commission, Henry Noel Brailsford, wrote: “In Europe the epoch of conquest is over and save in the Balkans perhaps on the fringes of the Austrian and Russian empires, it is as certain as anything in politics that the frontiers of our national states are finally drawn. My own belief is that there will be no more war among the six powers.”
And Ferguson re-tells the story of LTCM in his online article “Wall Street Lays Another Egg” (which also covers many of the other themes from his book):
…how exactly do you price a derivative? What precisely is an option worth? The answers to those questions required a revolution in financial theory. From an academic point of view, what this revolution achieved was highly impressive. But the events of the 1990s, as the rise of quantitative finance replaced preppies with quants (quantitative analysts) all along Wall Street, revealed a new truth: those whom the gods want to destroy they first teach math.
Working closely with Fischer Black, of the consulting firm Arthur D. Little, M.I.T.’s Myron Scholes invented a groundbreaking new theory of pricing options, to which his colleague Robert Merton also contributed. (Scholes and Merton would share the 1997 Nobel Prize in economics.) They reasoned that a call option’s value depended on six variables: the current market price of the stock (S), the agreed future price at which the stock could be bought (L), the time until the expiration date of the option (t), the risk-free rate of return in the economy as a whole (r), the probability that the option will be exercised (N), and—the crucial variable—the expected volatility of the stock, i.e., the likely fluctuations of its price between the time of purchase and the expiration date (s). With wonderful mathematical wizardry, the quants reduced the price of a call option to this formula (the Black-Scholes formula).
Feeling a bit baffled? Can’t follow the algebra? That was just fine by the quants. To make money from this magic formula, they needed markets to be full of people who didn’t have a clue about how to price options but relied instead on their (seldom accurate) gut instincts. They also needed a great deal of computing power, a force which had been transforming the financial markets since the early 1980s. Their final requirement was a partner with some market savvy in order to make the leap from the faculty club to the trading floor. Black, who would soon be struck down by cancer, could not be that partner. But John Meriwether could. The former head of the bond-arbitrage group at Salomon Brothers, Meriwether had made his first fortune in the wake of the S&L meltdown of the late 1980s. The hedge fund he created with Scholes and Merton in 1994 was called Long-Term Capital Management.
In its brief, four-year life, Long-Term was the brightest star in the hedge-fund firmament, generating mind-blowing returns for its elite club of investors and even more money for its founders. Needless to say, the firm did more than just trade options, though selling puts on the stock market became such a big part of its business that it was nicknamed “the central bank of volatility” by banks buying insurance against a big stock-market sell-off. In fact, the partners were simultaneously pursuing multiple trading strategies, about 100 of them, with a total of 7,600 positions. This conformed to a second key rule of the new mathematical finance: the virtue of diversification, a principle that had been formalized by Harry M. Markowitz, of the Rand Corporation. Diversification was all about having a multitude of uncorrelated positions. One might go wrong, or even two. But thousands just could not go wrong simultaneously.
The mathematics were reassuring. According to the firm’s “Value at Risk” models, it would take a 10-s (in other words, 10-standard-deviation) event to cause the firm to lose all its capital in a single year. But the probability of such an event, according to the quants, was 1 in 10^24—or effectively zero. Indeed, the models said the most Long-Term was likely to lose in a single day was $45 million. For that reason, the partners felt no compunction about leveraging their trades. At the end of August 1997, the fund’s capital was $6.7 billion, but the debt-financed assets on its balance sheet amounted to $126 billion, a ratio of assets to capital of 19 to 1.
There is no need to rehearse here the story of Long-Term’s downfall, which was precipitated by a Russian debt default. Suffice it to say that on Friday, August 21, 1998, the firm lost $550 million—15 percent of its entire capital, and vastly more than its mathematical models had said was possible. The key point is to appreciate why the quants were so wrong.
The problem lay with the assumptions that underlie so much of mathematical finance. In order to construct their models, the quants had to postulate a planet where the inhabitants were omniscient and perfectly rational; where they instantly absorbed all new information and used it to maximize profits; where they never stopped trading; where markets were continuous, frictionless, and completely liquid. Financial markets on this planet followed a “random walk,” meaning that each day’s prices were quite unrelated to the previous day’s, but reflected no more and no less than all the relevant information currently available. The returns on this planet’s stock market were normally distributed along the bell curve, with most years clustered closely around the mean, and two-thirds of them within one standard deviation of the mean. On such a planet, a “six standard deviation” sell-off would be about as common as a person shorter than one foot in our world. It would happen only once in four million years of trading.
But Long-Term was not located on Planet Finance. It was based in Greenwich, Connecticut, on Planet Earth, a place inhabited by emotional human beings, always capable of flipping suddenly and en masse from greed to fear. In the case of Long-Term, the herding problem was acute, because many other firms had begun trying to copy Long-Term’s strategies in the hope of replicating its stellar performance. When things began to go wrong, there was a truly bovine stampede for the exits. The result was a massive, synchronized downturn in virtually all asset markets. Diversification was no defense in such a crisis. As one leading London hedge-fund manager later put it to Meriwether, “John, you were the correlation.”
There was, however, another reason why Long-Term failed. The quants’ Value at Risk models had implied that the loss the firm suffered in August 1998 was so unlikely that it ought never to have happened in the entire life of the universe. But that was because the models were working with just five years of data. If they had gone back even 11 years, they would have captured the 1987 stock-market crash. If they had gone back 80 years they would have captured the last great Russian default, after the 1917 revolution. Meriwether himself, born in 1947, ruefully observed, “If I had lived through the Depression, I would have been in a better position to understand events.” To put it bluntly, the Nobel Prize winners knew plenty of mathematics but not enough history.
These episodes should remind us of the fragility of our current situation. Indeed, as one of many potential future scenarios, Ferguson candidly discusses the prospects for a serious breakdown in relations between China and the west, akin to the breakdown of relations that precipitated the First World War.
In summary: I recommend this book, not only because it is full of intriguing anecdotes, but because it will help to raise awareness of the complex impacts of financial systems. It will help boost general literacy about all aspects of money – and should, therefore, help us to be more effective in how collectively manage financial innovation.
Note: There are two editions of this book: one released in 2008, and one released in 2009. The latter has a fuller account of the recent global financial crisis, and for that reason, is the better one to read.