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Choosing between Cornell Applied Ops and BU MSMF

Which program would you choose


  • Total voters
    14
Dear Friends,

I have received offers from the abovementioned programs but am lost on which to choose. Ideally I hope to pursue a career in Quantitative research leading up to a role in portfolio management after graduation. I am also keen to refine my programming skills to pivot into a data science role should my plan not work out. With this in mind I would like to seek your advice on which program would be more suitable.

Personally, I am leaning towards Cornell due to its brand name as well as relatively decent career services and likely more support with the smaller cohort. On the other hand BU MSMF would definitely provide a more comprehensive training in quant finance and might be more applicable in landing a role in quantitative research. I do have some concerns about the placement statistics at BU as well as the purported grade deflation from past reviews.

Thanks in advance for your response.
 
I gave my vote to Cornell ORIE, because I believe it will open more doors in the future. However, if you are 100% sure you want to be a quant researcher, then BU might be more suitable, since it has a research track.

Just something to consider. When I asked my internship co-workers what they thought of Cornell ORIE > Quant (both of them hold advanced degrees at Cornell ORIE) they flinched and didn't think it was a good idea. They recommended me to go for an MS in applied math, physics, or a Masters in Finance.
 
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@lolalin May I ask what field your co-workers are in? Or if you have some information on the fields that ORIE degree holders pursue. Thank you.
They are in investment banking, think traditional IB like M&A, DCM, ECM. Although from my understanding they were recruited/handpicked by the boss.

A cursory search on linkedin tells me that many work in data science. I invite you to do the same search.

EDIT: As a bit of a clarification, they were already working as full time analysts/associates, not interns
 
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@lolalin Yes I have noticed that most of the OR grads seem to delve into data analytics upon graduation, but these seem to be the top performing students of their cohort. Was wondering if students do get shoehorned into supply chain roles.

@auroreaborealis I do believe that quant finance is a subset of ORIE, with the same technical skills required, but with a heavier emphasis on math and programming and less so in finance. However, with schools like BU and UCLA the program seems to be housed within the business school, which might result in less emphasis on training in quantitative methods which might limit post graduation employment to risk management roles. I'm happy to hear your opinion on this.
 
@lolalin Yes I have noticed that most of the OR grads seem to delve into data analytics upon graduation, but these seem to be the top performing students of their cohort. Was wondering if students do get shoehorned into supply chain roles.

@auroreaborealis I do believe that quant finance is a subset of ORIE, with the same technical skills required, but with a heavier emphasis on math and programming and less so in finance. However, with schools like BU and UCLA the program seems to be housed within the business school, which might result in less emphasis on training in quantitative methods which might limit post graduation employment to risk management roles. I'm happy to hear your opinion on this.
Well, Financial Engineering is technically speaking a subset of Operations Research, see: Operations research - Wikipedia.
I, however, do not know if they are shoehorned into supply chain, maybe email Cornell or reach out to graduates and find out.

Though, keep in mind that a there are top MFE programs housed in business school (though a lot are interdisciplinary.) For instance, MIT's MFin is housed in Sloan, Georgia Tech's program is housed in Scheller, CMU's is technically based in Tepper, and Berkeley is in Haas.
 
Google P vs Q from Attillio Meucci. Read it.

Quant in from an MFE program is going to involve a lot of stochastic calculus, risk neutral pricing. You're trying to figure out the market clearing price at a given moment in time.

The buy side is more concerned with the future price - so more time series, linear factor models - they are looking at connection between a given instant to predict future distributions. Machine learning, data science, etc are all quite relevant here.

So when you say quant research, you could be talking about options trading in a prop shop, which indicates MFE is the best choice. Same thing for a desk strat, low latency trading, etc.

If you're saying portfolio management, you may mean buyside traditional HF / asset management, in which case Data science might be more useful.

There definitely is a convergence - asset managers eventually have to trade to implement their strategies, so understanding market microstructure turns out to be very important.

TLDR: quant research means different things depending on the area. it's as helpful as saying "I want to work in finance"
 
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