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twofish-quant
#8
Jul7-10, 11:08 PM
P: 6,863
Quote Quote by BenTheMan View Post
This is a great book for taking your intuition about quantum mechanics and path integrals and applying it to finance. As physicists, we've spent a lot of time thinking about quantum mechanics. At the end of the day, though, math is just math. If you can parse finance in terms of something you already (presumably) understand, it will give you some intuition/frame of reference regarding the new types of problems you will be solving.
I'd put this into the category of "recreational reading." It's sort of interesting, but no one I know uses any of those techniques in practice.

Seems like a broad introduction, but I'm not very far into it just yet. Check phds.org---the bulk of the jobs are in high frequency/algorithmic trading. It might help to have some idea about the methods these guys use before going into a job interview.
Careful here. Jobs are like fashions. They change every few months. There was a burst of high frequency/algorithmic trading jobs a few months ago, but that seems to have cooled, and the jobs right now are in risk management and model validation. I'm sure that six months from now, that will have changed and there will be some new hot field.

If you are looking at something that you have no experience in, then the most useful think is to read the books enough so that you can know the major terms and the jargon. It's not that useful to try to develop deep knowledge of something you know absolutely nothing in, because if the job calls for deep knowledge in statistics and you don't have it already, you probably aren't going to get it, and you are better off interviewing for jobs that do focus on areas you have deep knowledge in.

On the other hand, you can very quickly learn the buzzwords and the terms, so that you can understand the question and communicate sensibly, and answer very basic questions. You aren't going to be an expert in statistics if you aren't one already, but you can do a quick google and understand what Granger causality and cointegration is. It's very superficial knowledge, but superficial knowledge is important because it allows you to communicate with an expert with deep knowledge.