From Quixotic Finance:
Donald MackKenzie: An Engine, Not a Camera: How Financial Models Shape Markets (Inside Technology), MIT Press, 2008
‘[T]he belief that a theory can be tested by the realism of its assumptions independently of the accuracy of its predictions is widespread and the source of much of the perennial criticism of economic theory as unrealistic. Such criticism is largely irrelevant, and, in consequence, most attempts to reform economic theory that it has stimulated have been unsuccessful.’
The quote above is from Milton Friedman’s influential, if not uncontroversial, 1953 essay ‘The Methodology of Positive Economics’. Friedman’s essay is still topical today, perhaps even more than when it was first published 60 years ago. In recent years there’s been a cottage industry of books attacking economists and quants in finance, accusing them of employing theories with suspicious assumptions, even to the point of suggesting that the supposedly ‘false’ theories (and their alleged proponents) are to be held responsible for the worst financial crisis in decades. At the root of that accusation lie a host of misconceptions, fallacies and popular myths surrounding financial theories, their use in universities or financial institutions, and the way financial markets operate.It takes very little knowledge or effort to spread around myths, especially ones that appeal to strong negative feelings about a particular group of people. It requires a tremendous amount of expertise, hard work and patience to unravel the facts and the fiction in a poisoned debate. Donald MacKenzie set himself a difficult task, but his ‘An Engine, Not a Camera – How Financial Models Shape Markets’ is a remarkable achievement.MacKenzie interweaves two related narratives in his book: that of the emergence of modern economics and theoretical finance, characterized by an abstruse (to outsiders at least) mathematical formalism; and, on the other hand, that of the dramatic growth in both size and complexity of financial markets, in particular the derivatives market. In that respect he covers similar ground to Peter Bernstein’s ‘Capital Ideas’ (MacKenzie acknowledges his ‘great debt’ to Bernstein in the first chapter). Unlike Bernstein, however, MacKenzie uses the historical accounts as an integral part of the argument for his unifying thesis, namely that financial models played a crucial role in molding financial markets into their current shape (I’ll come back to that in a moment).The book title ‘An Engine, Not a Camera’ is a direct reference to Milton Friedman’s essay. As Friedman argued, the point of a model, whether in economics or another area of science, is not to serve as a camera representing reality up to its minute details. Rather, its purpose is to function as an engine – a tool allowing us to make predictions, or to analyze reality. This grounding in its practical use is the very essence and raison d’être of any model. Idealizations, also disparagingly referred to as ‘false assumptions’ (especially in economics), are an inevitable component of any model, not just in economics, but in any science – even physics (frictionless surfaces in physics are the counterpart to finance’s frictionless markets – my own example). Dismissing a model on the basis of the falsehood of one of its assumptions misses the point entirely. Besides, making assumptions does not involve any commitment on the part of the modeler as to its literal truth.One example to clarify. Black, Scholes and Merton (BSM) made a number of unrealistic assumptions in their option pricing model, for example that trading incurs no cost, or that stock price returns follow a normal distribution (chapter 5 in MacKenzie is a long discussion of the development of the BSM model and its ancestors; as well as its impact on option trading). The limitations of the model are widely recognized (and even pointed out by BSM themselves!), but that didn’t stop the model from quickly gaining popularity. And it remains highly relevant till today. The assumptions can be relaxed, giving rise to extensions of the original model (e.g. options on dividend-bearing stocks). The normal distribution can be used as an ingredient of a distribution that contains fat tails; Merton’s own jump-diffusion model (1976) for example, is a combination of the original BSM (diffusion) model with jumps as an extra ingredient. Jumps are a direct way to reflect discontinuous (suddenly falling) stock prices and the resulting fat-tailed distribution. After the 1987 Wall Street crash, markets found also a more roundabout way to have option prices reflect fat tails, namely through the volatility smile.
The metaphor of a model as an engine is very apt in this context: the BSM model was never meant as a true description of reality; rather it’s an engine of analysis, a way of getting a grip on the myriad market forces affecting asset prices. BSM taught us we needn’t know the expected return of the underlying stock in order to agree on a fair price. They taught us volatility is a much more important variable. They taught us the principle of risk-neutral valuation. These things remain valid in more advanced models. And as a very literal application of the ‘engine’ idea: option prices are usually quoted in ‘implied volatility’ by using the BSM formula inversely (i.e. inferring the volatility from the market price rather than plugging in the ‘real’ volatility in the formula).MacKenzie takes Friedman’s engine analogy one step further. Could it be, he asks, that financial models are more than just tools for the financial modeler? Could it be that they help shape the environment, which (in a narrow view) they are meant to describe? For example, could it be that the Efficient Market Hypothesis (chapters 2,3,4,9) not only inspired the invention of index tracking funds, but actually led to more efficient markets? Or, as an example of an opposite effect: could it be that the assumption of continuously moving stock prices made the markets more sensitive to discontinuities (remember 1987)?
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