S&T: Need this to join quant desks?

3 questions:

  1. What are the different "quant" desks in S&T? (ex. I know equity derivatives is one of them)

  2. For these quant desks, would recruiters view a mathematics major or a computer science major more favorably?

  3. Which is more useful on the job, a mathematics background (with a firm grounding in stochastic calculus, differential eqs etc.) or a computer science background (with a firm grounding in Java, C++, etc.)

^For #3, I know that many people said that VBA is the only programming language you need to know in trading, so I was wondering if this implies that known Java and C++ would make only a small marginal difference in terms of helping oneself out on the job?

 

Are you people serious? There is a difference between a trading desk that requires quantitative ability and a legitimate quant trading desk. The latter is a desk where trades are executed via algos and requires knowledge of a programming language (normally C++). And there are also quantitative strategies desks where algos are created to exploit trends in the market.

Equity Derivs does not need a math or comp sci background by any stretch of the imagination.

How many of you people actually work in S&T?

 
Best Response
awm55:
Are you people serious? There is a difference between a trading desk that requires quantitative ability and a legitimate quant trading desk. The latter is a desk where trades are executed via algos and requires knowledge of a programming language (normally C++). And there are also quantitative strategies desks where algos are created to exploit trends in the market.

Algorithmic trading is not the only kind of quant desk. Another is Stat Arb, which is definitely not limited to cash equity.

 

Yes, it's fairly typical--for hitter derivatives groups, the way a director of quant research for that group described it was that when a trader doesn't know how to price something, they come to us. traders for derivs groups tend to come from engineering backgrounds so they can understand the language of the model (insane math), but they are distinctly different from quants that make models. The way that quant described it was that they feel bad when a trader blows up, but that's not their fault, it's the trader's fault, and they're happy when the model makes a shitload of money for the firm.

 

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