by skipping some chapters that are more advanced or niche), then one might say that there's 400 pages to read per book, so 1600 pages in total.īut you also need to do exercises since that's how you know that you're actually learning, and that's also how the interview will be like. Assuming that you don't finish the entire book (e.g. However, assuming these criteria are satisfied, I would recomment the following 4 books. And even if you are, two weeks is an extremely short amount of time. If he has an interest, he should def check out those opportunities as well.This is impossible unless you are very intelligent with good memory-retention skills and already mathemathically proficient in the field of analysis and statistics (and no, a single course in basic probability theory does not suffice). To me, it also provides an opportunity to really be innovative rather than make marginal improvements to an existing systematic method through algorithm changes or factor research. I've also heard that some of these fundamental managers are paying these guys handsomely. Unfortunately, many of these unconventional techniques and data sets are only applicable to a handful of companies or industries, which make them irrelevant to a quantitative fund that might have positions on hundreds of stocks. While they don't build systematic strategies using this information, they do use these "quant" techniques to identify opportunities. is currently being done by fundamental managers. Mining SEC filings and earnings calls, scraping website data from online retailers, etc.
Although I work at a purely systematic firm, I actually think some of the most interesting quantitative work is being done by fundamental managers ironically. I've heard of top fundamental/value funds like Baupost group that typically hire ex bankers hiring quants to explore large/unconventional data sets. These firms are even hiring strong fresh undergrads into roles previously only held by PhDs so he should definitely be able to land somewhere.Īnother thing that he should consider or at least be aware of is that even fundamental firms are starting to explore quantitative strategies that inform their fundamental process. Quant firms are pretty desperate for good talent. The market is very, very hot for quants at the moment so he should be optimistic. At most he should just be prepared to answer why he's interested in the firm or quantitative finance in general.Īnytime and absolutely (I'm in nyc)! I wish him the best of luck. TL DR: Know undergrad probability/statistics questions from Ross' books, be able to communicate how the modeling process works from start to finish (data cleaning, cross validation, variable attribution, parameter tuning, dimensionality reduction, etc.). You can still mess up a question or two and get the job if you demonstrate your problem solving ability through your communication. He should practice working through problems out loud so that he is communicating his thinking clearly. The math questions they ask are generally undergrad level but what makes them tough is you don't quite know what they are going to ask like you do on a college test. He should check glassdoor to get some example interview questions. If he's just out of grad school his raw math skills should hopefully be pretty sharp. Get shit data -> clean data -> try using a model -> model doesn't work -> realize data still shit -> clean data again -> try using a model ->. Although Citadel and DE Shaw have dedicated teams for data cleaning, I still think it's important. Being able to describe this process is important.
You can have the best model ever but if you aren't able to do basic checks on your data to make sure that it is even sensible then your model is useless. Being able to communicate the process of how you clean up data, do basic sanity checks, etc. If he's interviewing on the equities side, one big thing is dealing with messy data. Not sure about PIMCO or MSIM but I imagine that those will be a lot more behavioral. Citadel and DE Shaw are pretty purist about evaluating people using straight up tests. Companies also like to ask lots of combinatorics questions too so he should be prepared for those. Many of the questions asked in interviews are based off of exercises from these books. He should be very solid in upper level undergraduate probability theory (See Sheldon Ross's books on intro to probability and probability models). The harder questions are generally the math questions. They want to make sure that you can write clean efficient code. He should have familiarity with a lot of the basic computer science algorithms. He will need to prepare for both programming questions as well as mathematics questions.
0 kommentar(er)
