Do quantitative financial analysts with expertise in stochastic calculus have an advantage over analysts with expertise in machine learning (assuming both have similar programming skills)?

Aaron Brown, MBA Finance & Statistics, The University of Chicago Booth School of Business

Stochastic calculus was never an important practical skill. For one thing, almost no one can really do it. We only know the solutions to a few types of stochastic differential equations. What people call “stochastic calculus” consists mainly of recognizing when you can recast a problem as one of the known solutions, and how to fake it when you can’t. For another thing, not many places are interested in genuine financial research.

Nevertheless, stochastic calculus is hard, and an easily measured skill. It was always used more as an IQ test than something needed for the job. It is still respected on that basis. And since it’s central to the historical development of theoretical quantitative finance, anyone claiming financial quant skills who can’t solve textbook stochastic calculus problems is assumed to be unable to do it. I also think it’s worth learning because it does stretch your brain.

Machine learning is a popular term today, but it doesn’t have to be hard, and it’s not easily measured. Any average bright person can get a good grade in most machine learning courses. There’s no deep concepts to change your brain, there’s some cookbook approaches, some rules of thumb about which ones work, a ton of nonsense and some theoretic results of dubious practical use (in the history of machine learning, theory has more often misled development than informed it). I have no doubt that there are geniuses in machine learning around, but no one can tell them from the self-promoters and delusional optimists, and I wouldn’t be surprised if they are not the tenured faculty at name computer science departments.

Machine learning will certainly be more important to the future development of quantitative finance than stochastic calculus will, but claiming expertise in it is like claiming that you have a lot of good ideas. It’s an argument for why someone should hire you, not an objectively measurable skill; it says more about how you would approach a job than what you can do.

At the moment, lots of people are hired for “machine learning” the way Wall Street treats any claimed technical skill: hire a bunch of people and put them in IT, pay them well but don’t treat them well, keep them around if they produce useful code, maybe let a few of them near decision makers and perhaps a few will prove to be good traders or developers of good products; figure most of them will be gone in a few years, or unrecognizable as machine learning specialists.

There are some serious efforts to develop useful machine learning financial applications, but not that many. These places hire people who have worked on objectively successful machine learning projects, not people with top degrees or who won prestigious machine learning competitions. The general view seems to be that technical machine learning education is cheap to buy, but that some people seem to be able to lead successful projects, and others do not.

So as a resume line item, “stochastic calculus” commands respect, “machine learning” is only a notch above the flavor-of-the-month hobby. But true ability in stochastic calculus is rare, and not very useful. True ability in machine learning is probably more common, and extremely useful.

This question originally appeared on Quora




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