About James Owen Weatherall

Rocket Physics by Steve Jurvetson on Flickr

Blaming the quants for the recent financial crisis is simplistic and short-sighted, says the author of The Physics of Wall Street. He picks five books showing the contribution physics has made to understanding financial markets.

I wonder if you could give us a brief overview of the history of financial thinking and its cross pollination with physics?

The start of mathematical financial modelling is also the start of the cross pollination with physics. The first person who developed a serious mathematical model of a financial product was the French mathematician and mathematical physicist Louis Bachelier in 1900. He approached the problem of how much you should be willing to pay for an option an option being a contract to give you the right but not the obligation to buy or sell some security at a future time and at a price you determine in advance when you purchase the option. He had the idea that some basic statistical reasoning about probabilities should be used to work out how much that option should be worth. In particular, how its value should change over time. He developed this very striking connection between option pricing and certain ideas in statistical physics, and in thermodynamics in particular.

Bachelier was way before his time. No-one in mathematics and finance appreciated his work and it took half a century before he was recognised. In the 1950s, the great economist Paul Samuelson, the author of one my book choices, worked on the same sort of problem that Bachelier was interested in and, by chance, had his attention drawn to Bacheliers work. He managed to track down Bacheliers dissertation and discovered that, although he did find one major error in its reasoning, the basic outline was identical to what he was working on. So it was at that moment that the kind of methods that had been pioneered by Bachelier half a century earlier became widely recognised as relevant to financial economists.

By the 1960s Samuelson and others had developed a real appreciation of the importance of a certain type of mathematical reasoning in finance. Other mathematicians and physicists, notably MFM Osborne and Ed Thorp, also rediscovered some of the Bacheliers ideas and extended them. Thorp was the first to figure out how to take these abstract ideas and use them to make money. He started what is widely understood as being the first modern quantitative hedge fund in the late 1960s. However, much like Bachelier, he was a bit before his time. He wrote a book Beat the Market, another of my books choices, in 1967 but nobody paid any attention to it. It was only when the ideas of Bachelier, Samuelson and Thorp were repackaged by economist Myron Scholes and applied mathematician-turned-economist Fischer Black that their importance for practice was widely recognised by the big banks. The Black-Scholes paper was published in 1973, which, coincidentally, was the same year that the first US options exchange opened in Chicago. When the options market opened, banks found themselves drawn into a market they hadnt really participated in before. The Black-Scholes paper showed how ideas developed in maths and physics could be applied to understand option pricing. Things really accelerated from that point on.

Was the 2007/2008 financial crisis caused by a failure of these ideas?

There is a sense in which you can say that, but I dont like to as I think it runs together two separate issues. On the one hand is the reliability and value of mathematical models in general and the methodology that is used to construct mathematical models in finance. On the other hand, you have an entirely separate issue of how those models are used. I think a big part of what happened in 2007/2008, particularly in regard to mortgage backed securities, was far more to do with the misapplication of models than with the failure of models themselves.

In some sense the models did not give accurate predictions of the value of certain products, but they shouldnt have been expected to. Any mathematical model represents a simplification. One begins by making a number of strong assumptions about the market conditions one is operating under and the nature of the product one is using, and in the presence of those assumptions you come up with some set of equations that will provide useful information to you. But when those assumptions fail, you cannot expect those models to be reliable any longer. So what happened in 2007/2008 is that we continued using models that were designed for market conditions that disappeared in 2005. The problem had to do with the way people who were using these models failed to pay attention to the reliability of their assumptions.

So the problem lies with the people who use these models rather than the models themselves?

Many traders can have deep understanding and intuitions about how markets work but may not fully understand all the tools they are using, in particular some of the models their computer systems are using. As a result they are unable to recognise situations where the assumptions underlying the models begin to make them less reliable. But in lots of fields, not just finance, there are lots of people in jobs who do not understand the broader ramifications of what they are doing. Presumably an inability to see the long-term ramifications of your specific acts shouldnt single out finance over say politics or other forms of business.

But, putting politics and finance to one side, you could argue that the consequences of poor assumptions and modelling in other fields are generally not so catastrophic. In 2007/2008 most western economies were plunged into an unprecedented crisis.

I want to turn that on its head. Yes, its absolutely right that problems in financial markets have severe consequences and one has to be careful to avoid introducing new types of risks into markets. But one thing we have learnt in the twentieth century is that the general methodology underlying mathematical models in finance is really just the best collection of tools we have for learning about almost any subject. Whats the alternative? If we dont use mathematical models were stuck using gut instinct and intuition, which are certainly less sophisticated methods. Its not as if those dont have a role to play, but to say that using mathematical models is what led to the 2007/2008 crisis is short sighted. The benefits we have enjoyed from using these financial instruments over the last 40 years are such that their use is well justified.