There are things to quibble with, but I’m generally an Emanuel Derman fan. He’s one of the original physicists who became a “quant” for Goldman Sachs, and now has turned commentator about the virtues and perils of modeling. Specifically, he’s very good at explaining the role of math in forecasting capital markets.
Derman’s new book is pithy and well wrought—an easy one to knock out during the holidays.
I find folks in the finance field tend to treat data as “facts” sans the caveat that numbers are a kind of semiotics—representations; a step removed from reality and not reality as such. It’s one of the reasons, among many, I think it’s worth occasionally taking a look at current thinking in physics—which really starkly reveals what mathematics can and can’t do in terms of explaining the world. Physics is a place where imagination and creative thinking intersect with the rigid logic of math—it’s a style of thought much better suited to how to think about capital markets as opposed to rote statistics and engineering.
Said differently, how many times this year (or any year, really) have we seen some bulge bracket firm tell us how the world is going to work based on some ironclad multifactor regression model…only to see the world simply do something else? There’s a chasm of difference between math’s ability to quantify and categorize the world in order to better understand it, and the ability for math to say what happens next. One of the great next debates in financial theory will be about the prevalent illusion of validity tied to faith in numbers.
Emanuel Derman’s My Life as a Quant is a fun autobiographical book about his personal experience in finance as quant funds became vogue, but does no justice to the sheer brainpower and creative intelligence behind the man.
Luckily, he penned a column recently for Edge that does. In Metaphors, Models, and Theories comes an essay that’s simply devastating to the current norms of theoretical and practical thought in finance. Thus, my guess is it’ll go largely ignored because it essentially means many if not most Wall Street analyst jobs today are useless. But Derman’s view is righteous and impossible to ignore once you’ve experienced it.
I won’t summarize the piece because that might mean you won’t read it. What it does, though, is take finance, theories of value, and economics generally, back from the rigid, reductionist fascist tendencies of today’s mathematics fanatics, and puts finance instead squarely into the territory of phenomenological experience, practicality, and complexity. It demands vigilance and creative insight—concentrated consciousness upon the problems of markets—and a lack of reliance on equations or black box solutions (as so many wish there was, and many will always quest for). Derman’s essay is a high-minded, and sometimes overly technical (Derman’s reference to Spinoza’s attempt to systematize the emotional spectrum is, at this point, less relevant than contemporary philosophical/psychological work in this area, but it’s still worth the slog).
Real knowledge about markets isn’t just rote logic and empiricism—it’s often visceral, intuitive (but then again almost totally counter intuitive until you’ve spent forever studying markets, their history, what they do, why, their anomalies, and their universalities), experiential, and phenomenological. It’s a breath of fresh air to hear Derman champion the study of market history. Market forecasting and understanding is explicitly about what happens in the real world, and thus logic and math are secondary and inferior things. Models and math should be used lightly, with simplicity viewed a virtue, to judge relative conditions and act as reductionist descriptors. What Derman is saying is that there’s a very good reason Economics has forever been lumped in with the social sciences and not hard sciences, and no amount of mathematical workmanship can change that. Finance wants to be different but mostly isn’t.
Hardcore finance wonks should read this thing twice, then put it aside, and read it twice more. Don’t let these lessons escape you. Below are some favorite passages. Happy New Year!
- There is a gap between the model and the object of its focus. The model is not the object, though we may wish it were.
- A model is a metaphor of limited applicability, not the thing itself. Calling a computer an electronic brain once cast light on the function of computers. Nevertheless, a computer is not an electronic brain. Calling the brain a computer is a model too. In tackling the mysterious world via models we do our best to explain the thus-far incomprehensible by describing it in terms of the things we already partially comprehend. Models, like metaphors, take the properties of something rich and project them onto something strange.
- A model focuses on parts rather than the whole. It is a caricature which overemphasizes some features at the expense of others.
- A model is a fetish in which the importance of one key part of the object of interest is obsessively exaggerated until it comes to represent the object’s quintessence, a shoe or corset standing in for a woman. (Is that perhaps why most modelers are male?). But the shoe or corset isn’t the woman, just the most important part of the woman for this model user.
- Models are analogies, and always describe something relative to something else. Theories, in contrast, are the real thing. They don’t compare; they describe the essence, without reference. Every fact, as Goethe wrote, is a theory.
- …A theory is the ultimate non-metaphor.
- Theories tell you what something is. Models tell you only what something is more or less like. Unless you constantly remember that, therein lies their danger.
- Intuition is a merging of the understander with the understood. It is the deepest kind of knowledge.
- No model is correct – a model is not a theory – but models can provide immensely helpful ways of initial estimates of value.
- There are no genuine theories in finance. Financial models are always models of comparison, of relative value. They are metaphors.
- All concepts, perhaps all things, are mental. But there are no genuine theories in finance because finance is concerned with value, an even more subjective concept than heat or pressure. Furthermore, it is very difficult to find the scientific laws or even regularities governing the behavior of economies: there are very few isolated economic machines, and so one cannot carry out the repeated experiments that science requires. History is important in economics. Credit markets tomorrow won’t behave like credit markets last year because we have learned what happened last year, and cannot get back to the initial conditions of a year ago. Human beings and societies learn; physical systems by and large don’t.
- The greatest conceptual danger is idolatry, imagining that someone can write down a theory that encapsulates human behavior and relieves you of the difficulty of constant thinking. A model may be entrancing but no matter how hard you try, you will not be able to breath true life into it. To confuse the model with a theory is to embrace a future disaster driven by the belief that humans obey mathematical rules.
- Financial modelers must therefore compromise, must firmly decide what small part of the financial world is of greatest current interest, decide on its key features, and make a mock-up of only those. A model cannot include everything. If you are interested in everything you are interested in too much. A successful financial model must have limited scope; you must work with simple analogies; in the end, you are trying to rank complex objects by projecting them onto a low-dimensional scale.