Amazon’s description of the book: In My Life as a Quant, Emanuel Derman traces his transformation from ambitious young scientist to managing director and head of the renowned Quantitative Strategies group at Goldman, Sachs & Co.
Here are a few good quotes from the book, regarding software development, working at Goldman Sachs, working with Fischer Black and financial models.
- Impossible for me to over look the difference that a simple and well designed piece of software can make to a business.
- I always included a bit of code my models that kept a log of who used it and when, which provides documented proof of utility even it does attach a dollar value.
- Perturbation theory: The approach by which you get the most critical feature done first, then each next step you tackle the next most important feature.
On working with Fischer Black
- Stickler for precision.
- Liked to think everything through for himself.
- Terse, good natured conversational writing style using clear but casual unadorned English.
- Devoted to clarity and simplicity.
- Rather guess what follows relevant assumptions than derive precise conclusions from less-relevant assumptions.
On working for Goldman and Solomons.
- At Goldman the enemies were the competing firms, at Solomons the enemies were competing colleagues.
- Goldman was long term greedy rather than short term greedy. At Solomons I thought it was every man for himself and God against them all.
- Goldman aimed to take the appropriate level of risk, not eliminate it. No risk, no return.
- Models are only models, toylike descriptions of idealised worlds
Slowly it began to dawn on me that what we faced was not so much risk as uncertainty. Risk is what you bear when you own, for example, 100 shares of Microsoft—you know exactly what those shares are worth because you can sell them in a second at something very close to the last traded price. There is no uncertainty about their current value, only the risk that their value will change in the next instant. But when you own an exotic illiquid option, uncertainty precedes its risk—you don’t even know exactly what the option is currently worth because you don’t know whether the model you are using is right or wrong. Or, more accurately, you know that the model you are using is both naïve and wrong—the only question is how naive and how wrong.
I think this is the right way to use mathematical models in finance. Models are only models, not the thing in itself.We cannot, therefore, expect them to be truly right. Models are better regarded as a collection of parallel thought universes you can explore. Each universe should be consistent, but the actual financial and human world, unlike the world of matter, is going to be infinitely more complex than any model we make of it.We are always trying to shoehorn the real world into one of
the models to see how useful an approximation it is.
You must always ask: Does the model give you a set of plausible variables to describe the world, and a set of relationships between them that permits its analysis and investigation? You’re always trying to make a limited approximation of reality, using variables that people can comprehend, so that you can say to yourself or your boss, for example, “I was short emerging-market volatility, so we lost money when the crisis came.” Good theories, like Black-Scholes, provide a laboratory of ideas in which you can work out the likely consequences of possible causes. They give you a common language with which to quantify and communicate your feelings about value.