MIT vs. Princeton vs. Yale undergrad for quant hedge fund

hnymvvn's picture
Rank: Chimp | 13

Hi all,

I am a current high school senior choosing between attending MIT, Princeton, or Yale for my undergrad. My current goal throughout and post undergrad is to secure a summer internship at one of the big NYC quant hedge funds (Jane Street, D.E. Shaw, Two Sigma) and hopefully transition into a full-time offer after graduating. Out of my three choices, which college would be most conducive to this kind of career path, and what majors and activities specific to this college choice would be most conducive to this kind of career path? To a lesser degree, which college would provide the most versatility if I decide to deviate from this career path and do something else within STEM? I am mainly choosing between MIT and Princeton and have knocked Yale out of the equation, but if Yale has some low-key advantages that I don't yet know about, then I am willing to reconsider it.

I should add that at any of these colleges, I intend to major in math or any interdisciplinary subject that is primarily math. I am also considering ORFE at Princeton.

I would greatly appreciate any advice. Thanks for your help!

Comments (29)

May 5, 2018

Probably MIT by a wide margin, but all these schools place well into quant funds.

May 5, 2018

MIT. Major in anything, learn how to program. Avoid doing pure CS - you'll be typecast.

PS. I'd only pick D.E. Shaw of these 3 - definitely avoid Jane Street since that would put you on the HFT track and that's not really finance.

PPS. Also, instead of taking an official internship, try to find a spot on a smaller PM team - you will get more exposure to actual alpha generation.

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May 7, 2018

Why would you avoid Two Sigma?

May 7, 2018
DeepLearning:

Why would you avoid Two Sigma?

It's a great place to work, but it's not a good place to learn, especially at the beginning of your career. IMHO, of course, but I don't think people that work there get a broad enough exposure since it's more of a layer setup. At other places you get to see the full life cycle of a portfolio which is very valuable.
PS. I have never worked there myself and simply formed my opinion based on the people I've interviewed or spoken too

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May 7, 2018
Mostly Random Dude:
DeepLearning:

Why would you avoid Two Sigma?

It's a great place to work, but it's not a good place to learn, especially at the beginning of your career. IMHO, of course, but I don't think people that work there get a broad enough exposure since it's more of a layer setup. At other places you get to see the full life cycle of a portfolio which is very valuable.
PS. I have never worked there myself and simply formed my opinion based on the people I've interviewed or spoken too

Could you speak a little bit more to this? I've been considering putting myself in the interview process there.

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May 7, 2018
DeepLearning:

Could you speak a little bit more to this? I've been considering putting myself in the interview process there.

Big place, very industrialized, very assembly-line-like. There are places like that are great to work for (e.g. Two Sigma or RenTech) and there are places like that are outright horrible to work for (e.g. Worldquant). However,
(a) Since the research guys are separated from the actual traders and PMs, you will learn a lot about your own little niche but will not gain general exposure. Which means you will become a great researcher, but very unlikely to become a PM.
(b) There is also a matter of "credit when credit is due". In a place that is built like The Borg (very efficient, but also very integrated), you have zero linkage to your alpha and thus your payout becomes a function of your political talent. Also, since the place is large, you bargaining power is rather low.

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May 7, 2018

Two Sigma is fully systematic so research is where the $ is. The trading/PM side of two sigma is just operations. Becoming a PM is not the goal of a place like Two Sigma. Those with the title of portfolio manager oversee the portfolio as well as the research but the research is the key aspect. When doing quant research, you have to test how your ideas work given the full systematic strategy so you do still get a full picture of how the strategy you support works. Need to determine correlation to other factors, exposures, etc. so I don't think what you are saying is true. Being a "trader" at two sigma is not a sought after position

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Best Response
May 7, 2018
DeepLearning:

Two Sigma is fully systematic so research is where the $ is. The trading/PM side of two sigma is just operations.

While I understand the academic sentiment that execution is boring, it's an integral part of extracting alpha. If you have never executed trades yourself (i.e. debugged the process, optimized it etc), you have an incomplete picture of the world. Many shops are fully systematic, but not all of them separate research layer from execution layer. There are fully-systematic multi-manager shops, for example, where each team is pretty small and does everything themselves.

DeepLearning:

Those with the title of portfolio manager oversee the portfolio as well as the research but the research is the key aspect.

The PMs at TwoSigma are no different from senior PMs at other places. They oversee the full life cycle of the book, from the idea stage to risk management. Research is important, but at the end, it's about the PnL.

DeepLearning:

When doing quant research, you have to test how your ideas work given the full systematic strategy so you do still get a full picture of how the strategy you support works.

Not really, since in an integrated model of alpha factors (which is what TS is), you don't really follow your alpha through the pipeline. If you are working on alpha factors, you are literally producing a stream of numbers that has to be predictive of stock returns in a statistically meaningful way. If you are working in portfolio formation, you assemble these factors into large books, apply portfolio construction techniques, hysteresis and various other tricks, but you don't really know what's involved in finding these factors. If you work on the execution layer, you are tweaking algos to achieve the minimum impact and adding micro-alphas, but you don't really know why you're buying or selling these stocks.

It's a very efficient model and no single employee is irreplaceable and no single person (except for the PMs) is actually able to grasp the big picture (so the IP leakage is well contained). Some funds with that structure have realized that researchers are fungible and run it as a sweatshop (e.g. WQ) while some have not.

DeepLearning:

Being a "trader" at two sigma is not a sought after position

My point is that if you are planning to stay at TwoSigma for the rest of your career, you have nothing to worry about as long as you don't get fired and TwoSigma sticks around. Your career progression is rather limited from junior researcher to senior researcher. While on that ladder, you are removed from whatever PnL your alpha is producing and you have limited leverage to improve the situation.

On the other hand, if you go to another shop and work for a smaller, "do it all" PM group, you will get exposure to all aspects of extracting alpha, from idea generation to fine-tuning execution. You also get to interact with the PM directly most of the time, which is a massive learning opportunity early in your career. With that kinda of experience, you can at some point hang your own shingle if you have the stomach for it, join a multi-manager shop as a PM or keep doing what you're doing.

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May 5, 2018

MIT

May 5, 2018

Thanks for the opinions guys! I've been looking around at the course/major requirements at MIT and I think I'd be double majoring in pure math and in finance at Sloan. I'd still want to hear some more opinions/arguments before I decide, though. Given that there doesn't seem to be much CS at all in my double major plan, would I still need some CS component to my education to get an edge in this career path? How does this compare to something like math or ORFE at Princeton?

May 6, 2018

No point in doing finance for quant funds, you should do EECS or something and you definitely need programming. Much more useful to focus on CS instead of finance

May 6, 2018

Princeton.... ..

May 6, 2018

Why?

May 6, 2018

Hi, current senior at MIT studying computer science here. I have some inherent bias, but I'll give it to you straight from my perspective. To give a little more background on myself, I've interviewed/interned with a few quant places, but have come to focus much more on fundamental/value investing as my interests and understanding of investing has progressed through college. That being said, I have several friends working at DE Shaw/Jane Street/Two Sigma/AQR etc.

For your stated interest of working in quant, MIT is hands down the best of the three. This is for many reasons. First, any quant firm you'd ever be interested in aggressively recruits from MIT. Second, an MIT education in a technical subject, if taken seriously, will prepare you to do very well in a quant role. Third, quant is very much part of the finance culture at MIT. 80% of people pursuing careers in finance are looking for quant roles, and only about 20% are like me and pursuing a corporate finance/fundamentals role (value HF, IB, PE, etc.).

That being said, simply going to MIT is just the beginning of the journey in regards to landing a buy-side quant role out of college. Quant interviews are really tough. They generally expect that you have a solid grasp of the principles introduced in MIT's intro probability class (https://ocw.mit.edu/courses/mathematics/18-440-pro...) and often times will expect you to have coding literacy (usually Python or R, but sometimes C++ for more high frequency stuff). Further, because so many people at MIT are pursuing roles in quant finance, the GPA expectation is really competitive. DE Shaw basically will not look at anyone from MIT that does not have a GPA above 4.8/5.0 (the same as a 3.8/4.0, MIT just uses a different scale) and almost exclusively takes people that have a perfect 5.0/5.0. This is because they have no reason to take anyone with a non-perfect GPA because there is such strong interest in working in quant finance. Beyond this, internship experience is huge (as always). A lot of the quant funds also like to see that you have explored trading/investing on your own. If this intrigues you, I'd be happy to send you some academic papers or books that I used to experiment with quant trading on my own. However, working at a less famous quant fund is much less competitive and pretty much anyone at MIT who majors in a technical subject with decent grades and some internships can get a quant finance job somewhere.

Hope this helps! Happy to answer more questions.

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May 7, 2018
kharnn:

DE Shaw basically will not look at anyone from MIT that does not have a GPA above 4.8/5.0 (the same as a 3.8/4.0, MIT just uses a different scale) and almost exclusively takes people that have a perfect 5.0/5.0.

As scary as this sounds, I can actually somewhat corroborate this. This isn't just the case for D.E. Shaw's quant roles, but also for its fundamental equity hires. Although this sounds difficult to achieve, getting into a quant HF in the first place is a complete shot in the dark -- attending MIT will at least provide a concrete quant recruiting pipeline and provide a glimmer of light at the end of the tunnel.

All things considered, I'd still say that MIT is positions you best, 1) for its top-notch Math and CS programs, 2) access to Sloan. I also echo the sentiments about learning data science and statistics. The quant skill set is certainly differentiated from the bulk of material learned in typical CS programs, so a CS degree isn't necessarily required. A big congratulations, by the way @hnymvvn! All excellent schools so you can't really go wrong, but I'd still vouch for MIT.

Array

May 6, 2018

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May 7, 2018

Any of those schools will give you the opportunity to get into the industry. It comes down more to how you actually perform and develop yourself at those schools. Pick the one where you think you'll be happier (can be for reasons as silly as you like the architecture or the food or the dorm arrangements better). If you're somewhere that makes you happy, it becomes so much easier to focus on learning, and that's what really matters. How much knowledge and skill you can accumulate in your 4 years there to make yourself someone who can really add value. For example if you hate everything you aren't going to be in the right mental state to perform at your highest level academically.

If i had 4.0 math/physics/CS resumes from each of those schools, I would base my decision on who was the most intellectually curious, who has done cool projects that show they are an independent internally-motivated learner, who is more well-read and has a diverse knowledge base to draw on, etc. Not so much the name of the school, but the quality of the person themselves.

All the things you mentioned, like clubs and how the school is perceived by recruiters and firms, I'm not saying they don't matter, but the impact is marginal compared to how you actually perform. And it's my opinion that your capacity for peak performance can be heavily influenced by seemingly silly and insignificant factors that affect your happiness and mental state. Youre looking to optimize across all these factors like majors and clubs but i would encourage you to focus on optimizing your pure performance and intellectual development.

May 7, 2018

If you go to any of those schools, you will certainly be able to land interviews at all of the firms you mentioned. If you take rigorous coursework in math, stats and computer science and study extremely hard for the interviews, you can get into these firms from any of those schools. I wouldn't base your decision off which places better into quant funds. Go to the school you like the most.

May 19, 2018

Yale is no way near the same level as Princeton and MIT when it comes to quant HF. Go with those two.

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May 20, 2018

^

May 31, 2018

Did you get into all of these schools? Isn't the national commitment day over already?

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Jun 5, 2018
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Jul 31, 2018