George Soros bet the farm in 1992. Using more than the entire capital of his Quantum Fund, a hedge-fund named in honor of Werner Heisenberg’s “uncertainty principle” (physics will re-appear in this blog), Soros wagered $10bn that the British pound would be devalued. He was right. According to Niall Ferguson, “Soros reasoned that the rising costs of German reunification would drive up interest rates and hence the Deutschmark.” Aside from netting Soros a tidy sum, his bet forced the pound to be devalued and knocked Britain out of the European Rate Mechanism causing interest rates to decline, and shortly thereafter, took Britain out of recession.

Soros is also famous for his concept of “reflexivity” where perceptions shape reality. Essentially, market participants (you and I) operate with a bias. This bias can influence events and “…create the impression that markets anticipate future developments accurately…” However, the “Efficient Market Hypothesis” (somewhat of a misnomer) notes that markets are unpredictable. This is because at any point in time stock prices “…reflect all information relevant to their value, so that their prices change only on the receipt of new information which by its nature is random…this so-called random walk theory is incompatible with the notion of stock market bubbles, since during bubbles investors react to changes in share prices rather than new information…” (see page 330, of Devil Take the Hindmost, A History of Financial Speculation by Edward Chancellor).

Commenting on reflexivity, The Economist states that “once people come to believe that house prices never fall, they will buy too much property – and house prices will fall. When they believe that shares always do well in the long run, they will buy too many shares…,” and we know what follows that.

But why do so many “high caliber” (see previous “Revolutionary Road” blog) financiers and bankers make disastrous goofs from time to time? They forget to ask the “Silly Question” (see prior blog) and rely too much on complex, often flawed, mathematical formulas which can only be understood if you happen to know some physics, differential equations and calculus.

Before we proceed, let’s steep back a bit…

Economic historians trace the origin of modern stock exchanges to1602, the year in which the Dutch East India Company was formed. Voyages to the east were lengthy and hazardous, and the invention of the “joint-stock company” was a means of pooling resources and in a sense, spreading the risk – thereby minimizing the risk any one investor would be exposed to. A stock represents ownership of a company. My risk in the venture is limited to the amount of money I have invested.

According to Niall Ferguson, “ownership of the Company was thus divided into multiple partijen or actien, literally actions (as in ‘a piece of the action’).” This was followed by the creation of a secondary market (the Amsterdam Bourse) in which these “shares” could be bought or sold – the first stock market. Although the Romans engaged in financial transactions in the Forum where money could be exchanged, lent and invested.

In Going Dutch, How England Plundered Holland’s Glory, Lisa Jardine notes that the Amsterdam Bourse (stock exchange) “…was a place to charter ships, to insure their cargos, to obtain credit, to make payments, to rent warehouse space and to hire labourers for loading and unloading vessels…The Bourse was also where information of all kinds, from all around the globe, was exchanged and discussed, and turned into knowledge of prices, markets and trading opportunities.”

It is at this point that assessing risk is still rather simple. If I know that only one voyage out of ten succeeds, I have a 90% chance of losing my investment. But what happens as what I invest in becomes further removed from any actual underlying asset or business venture? What is the underlying asset behind a “liquidity put” or a “LIBOR-cubed swap?” From what assets do these financial instruments derive their value?

Like the game of craps where I can place many types of bets on what is essentially one throw of the dice, stock markets began to invent different ways of investing in one venture to appeal to people’s appetites for speculation. This was accomplished through the creation of the derivative, which was born out of speculation. Edward Chancellor observes that “…the Exchange became a crucible for speculative activities. Futures contract – agreements to deliver or take delivery of a commodity at a fixed price some date in the future – were common.” A futures contract is an example of a derivative. Something that “derives” its value from some underlying asset, such as a stock certificate, a commodity such as corn or even real estate.

But how should the price of a derivative be determined? Skipping the next 370 years, in 1973 Fischer Black and Myron Scholes worked out how to use share prices to calculate the value of derivatives in what is now known as Black-Scholes. Their calculations use partial differential equations and Brownian motion (like the theory of entanglement, physics pops up again) and can be seen here.  According to The Economist (In Plato’s Cave, January 24, 2009), ” it is as if you had a formula for working out the price of a fruit salad from the prices of the apples and oranges that went into it…Confidence in pricing gave buyers and sellers the courage to pile into derivatives.”

Myron Scholes went on to work with Robert Merton at Long Term Capital Management (LTCM) in the hopes of turning Black-Scholes into a cash register. In 1998, one year after Scholes and Merton received the Nobel Prize in economics for their work in the development of the derivatives market, Russia defaulted on its debt and the markets bolted for safety, wrecking LTCM. The Federal Reserve Bank arranged a $3.6bn bailout and investors saw their holdings plummet from $4.9bn to $0.4bn. According to Chancellor, Alan Greenspan told Congress only a few weeks before the bailout that hedge funds were “strongly regulated by those who lend the money.”

And here is the flaw in the model – LTCM’s models calculated that the loss it suffered was so unlikely it should never happen. “But that was because the models were working with just five years’ worth of data. If the models had gone back even eleven years, they would have captured the1987 stock market crash. If they had gone back eighty years they would have captured the last great Russian default, after the 1917 Revolution” notes Niall Ferguson.

So this is where we find ourselves. From The Economist again, “…the idea behind modeling got garbled when pools of mortgages were bundled up into collateralised-debt obligations (CDOs)…a typical CDO might receive income from several hundred sources…” making it “…impossible to model in anything but the most rudimentary way…neither could the models take account of falling mortgage-underwriting standards.”

Edward Chancellor, writing in 1999, observes that “financial risks that were formerly well understood have become arcane…new derivatives serve no other purpose than to facilitate speculation – in particular, enabling fund managers to circumvent prudential restrictions on their investments…the former head of the New York Federal Reserve Bank…warned that ‘the increasing complexity of the financial markets could override the ability of the most sophisticated efforts to monitor and manage risk’…the Federal Reserve, however, saw things differently and headed off moves to regulate the over-the-counter derivatives market.”

George Soros addressed “…the desirability of regulation to the House Committee on Banking in April 1994, but the Republicans’ capture of Congress later in the year – assisted by generous campaign contributions from other hedge fund managers – killed off any further moves to regulate hedge funds. Alan Greenspan of the Federal Reserve also lobbied against hedge fund regulation on the grounds that it would only send the hedge funds offshore (where most were already registered in order to avoid the scrutiny of financial regulators).”

 Thinking Around the Bend (Rather Than Outside the Box)  To followup on the physics theme, Soros’ notion of reflexivity is somewhat related to Schrödinger’s cat, a thought experiment involving quantum mechanics and Heisenberg’s “uncertainty principle.” The uncertainty principle basically states that you can never accurately know both a sub-atomic particle’s speed and position (“pairs of conjugate variables”) at the same instant in time. The more accurately you know the position of a particle, the less accurately you can know its momentum. This has something to do with the wave nature of particles, but I didn’t pass my university quantum mechanics class and can’t help you out…Getting back to the cat, to try and explain some of quantum mechanics’ absurd implications, Schrödinger imagined a cat in a box that was both alive and dead at the same time until you opened the box and observed which state the cat was in (by the way, I love cats – we have two of them). The cat has no reality unless it is observed. This prompted Einstein’s famous quote “God does not play dice.” But investors do, and it is not through observations but through their perceptions that investors affect market outcomes – which is reflexivity.