Thread by @choffstein

1/ I constantly get questions from people looking to go into graduate financial engineering (“FE”) programs.

I thought I’d compile my thoughts into a thread 🧵

2/ For context, I graduated from Carnegie Mellon’s MS in Computational Finance program in December 2010.

It was the world’s first FE program and, at the time, ranked #1.

Everything that follows is just my opinion based upon my experience.

3/ What are these degrees? They’re interdisciplinary studies (typically finance, mathematics, and computer science) that try to prepare someone for a career as a “quant.”

4/ Let’s start with the bad. These degrees are:

- Very expensive
- Externally Niche
- Internally Broad

5/ What do I mean by “Externally Niche”?

Outside of select circles, most people have no idea what these programs are. After initial job placement, it’s all on your own merit and experience. No fancy, three-letter credentials for you!

Enjoy being overlooked for CFAs!

6/ This degree confers highly specific knowledge. Be sure this is the career you want.

Friends that once worked on event driven desks, pricing complex derivatives, or in asset management now work in consulting, health care data analytics, and running real estate businesses.

7/ These degrees are very placement focused. Which means the curriculum is going to change over time.

When I went, it was all about complex derivatives because the banks hadn’t been fully dismantled yet. No data science. No machine learning. They still taught C++.

8/ In other words, a lot of the what you learn may quickly get out-dated for future career needs.

(I still don't see any crypto on the curricula…)

9/ What do I mean by “Internally Broad”?

Quant is a massive landscape. Are we talking a career in systematic long/short equity? Trading vol? Working on an interest rates swaps desk? Event-driven arbitrage? Risk modeling? Complex derivative pricing? Structured products?

10/ You’ll get an introductory touch on a lot of these topics, but never really go deep on any of them.

And it's a lot more theory than practical experience, so get a good internship.

11/ This means a lot of what you learn isn't going to be applicable in your career.

I spent 3 semesters learning Stochastic Calculus. Not sure I've used it since 2011.

(That said, learning for the sake of learning is wonderful and you never know where things might come up.)

12/ Given all this, I typically don't recommend anyone go into these programs unless they *really* know this is a life-long career decision and can get into one of the top programs.

13/ “Wow, Corey. You’re a downer. Is there anything good about these programs?”

ABSOLUTELY!

14/ There are few other degrees that give you the same cross disciplinary application.

You get finance, statistics, mathematics, and computer science all in one.

15/ e.g. one project had us price a product that was sold 2y puts on 3 stocks and then, after a year, went long 1y calls on the 2 worst performing stocks.

We had to numerically estimate price (delta + gamma), volatility, and correlation risks.

16/ Sure, you could go get an Applied Mathematics or Applied Statistics degree, but you're going to miss the specific domain knowledge that's key for derivatives pricing.

17/ And that's where these programs really seem to shine: if you *know* you're going into a derivatives heavy area.

We're talking structured product development, risk modeling, valuation, or trading.

(Though it largely depends upon the type of trading.)

18/ Yes, the curricula are changing to include more data science and machine learning, but I'm not sure all the fancy derivatives math is really necessary if you're just going to be working in less derivative-heavy areas (e.g. systematic equities).

19/ And if you're thinking HFT or market micro structure, I suspect Computer Science / Computer Engineering are more relevant.

(Hopefully someone else can chime in here.)

20/ Have I maximized the potential use of my degree in my own career? Probably not. Not enough derivatives work.

That said, I am very grateful for the foundational knowledge it gave me.

21/ So, in conclusion:

While expensive and niche, I believe this degree is useful for those who absolutely know they want a career where derivatives pricing is important.

22/ I’d love for other quants to chime in with their own experiences here, whether they pursued a similar degree or not!

cc @bennpeifert @jordanplatts