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Final target is to get into buy-side
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COMPARE MSCF vs MIT MFin(18 Months) vs UCB 2022 vs Harvard Health DS
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Which part of the Buyside? PE/VC, fundamental would be MIT, quant would be MSCF from my point of view. I think the Corp finance part of MIT and broad exposure to the entire field is a plus. MSCF has a tightly knit curriculum, with solid intros to data science / ML.
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Do you think the Harvard brand can help?
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Quant in buy side. MIT MFin has a FE track, how's it compared with MSCF? Thanks.
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Can't speak for the MSCF, but on the MFin, the FE track is very very flexible. You can take any main campus classes (Course 18, maths, course 6, computer science), which means you can really make the degree into whatever you want. Economic PhD courses (Econometrics, Time Series, Asset Pricing) all available too, so I'd imagine the curriculums could line up to be pretty similar if you wanted them to be?
MFin probably doesn't send as direct a signal to potential employers that you're dead set on quantitative finance, though.
@devconnolly said it well. fwiw, I'm also quant buy-side.
One thing to consider is career path - maybe consider going into sellside before buyside. The quant trading, especially in Fixed Income, is quite legit. You'll come out of there with solid skills. And if you're not as competitive right out of the gates, then risk / analytics is a solid back up. There are a lot more jobs on the sellside than on the buyside, and a stronger pipeline. It's a well worn path to go from sellside to buyside, but not really vice versa.
From my personal point of view, Corporate finance, which MIT has as a requirement, is critical for success on the buyside. This view is not in line with the hiring market view, however. Many quant managers, especially hedge funds, prioritize pure computational / statistical knowledge over corp finance knowledge. I already had corp finance background from CFA / MBA, so I was more concerned with finding a program that would provide a strong foundation for derivatives pricing, trading, and econometrics / trading / etc. I think there's a lot of opportunity in the convergence of sell-side short term risk neutral frameworks with longer term probabilistic sell side forecasting frameworks. CMU was my top choice from that perspective.
CMU has done well w/ adding ML / DS courses early on. I'm struggling more than I expected w/ the Sto_Cal components, but the teaching and support are top notch. I'll be glad to have completed those courses, and I'm looking forward to time series, asset management, and risk management courses later in the curriculum. Already my skills are magnitudes improved, and I expect to come out of here with the ability to look at portfolio management from an end to end perspective.
CMU's course plan is tightly integrated; which is great from my perspective because the courses are designed with respect to each other. Since the program is a partnership of 4 departments, you don't have the challenges of some other schools which have competing quant finance programs (one in the engineering dept, one in the stats / math dept, and one in the business school).
The only drawback is there is the limited ability to customize the course plan - to Devin's point above. But they've put a lot into the curriculum planning, so it wasn't a deal breaker for me.
You kind of have a champagne problem here - so congrats on that.
Re other options:
-I read NYU Courant is making substantial changes to the curriculum - I asked on here, and their response was kind of pissy. Which is weird, because they had an announcement on LinkedIn the week before. Incredible math foundation there, and worth looking into depending on the curriculum.
-Harvard's health data science is interesting, but my impression was that was more for PhD's / etc to get additional training. If you can stomach 3 years of grad school, I would seriously consider going for a PhD and doing an additional masters degree in ML / DS along the way. 2 masters degrees != PhD (believe me, I know about this).
One thing to consider is career path - maybe consider going into sellside before buyside. The quant trading, especially in Fixed Income, is quite legit. You'll come out of there with solid skills. And if you're not as competitive right out of the gates, then risk / analytics is a solid back up. There are a lot more jobs on the sellside than on the buyside, and a stronger pipeline. It's a well worn path to go from sellside to buyside, but not really vice versa.
From my personal point of view, Corporate finance, which MIT has as a requirement, is critical for success on the buyside. This view is not in line with the hiring market view, however. Many quant managers, especially hedge funds, prioritize pure computational / statistical knowledge over corp finance knowledge. I already had corp finance background from CFA / MBA, so I was more concerned with finding a program that would provide a strong foundation for derivatives pricing, trading, and econometrics / trading / etc. I think there's a lot of opportunity in the convergence of sell-side short term risk neutral frameworks with longer term probabilistic sell side forecasting frameworks. CMU was my top choice from that perspective.
CMU has done well w/ adding ML / DS courses early on. I'm struggling more than I expected w/ the Sto_Cal components, but the teaching and support are top notch. I'll be glad to have completed those courses, and I'm looking forward to time series, asset management, and risk management courses later in the curriculum. Already my skills are magnitudes improved, and I expect to come out of here with the ability to look at portfolio management from an end to end perspective.
CMU's course plan is tightly integrated; which is great from my perspective because the courses are designed with respect to each other. Since the program is a partnership of 4 departments, you don't have the challenges of some other schools which have competing quant finance programs (one in the engineering dept, one in the stats / math dept, and one in the business school).
The only drawback is there is the limited ability to customize the course plan - to Devin's point above. But they've put a lot into the curriculum planning, so it wasn't a deal breaker for me.
You kind of have a champagne problem here - so congrats on that.
Re other options:
-I read NYU Courant is making substantial changes to the curriculum - I asked on here, and their response was kind of pissy. Which is weird, because they had an announcement on LinkedIn the week before. Incredible math foundation there, and worth looking into depending on the curriculum.
-Harvard's health data science is interesting, but my impression was that was more for PhD's / etc to get additional training. If you can stomach 3 years of grad school, I would seriously consider going for a PhD and doing an additional masters degree in ML / DS along the way. 2 masters degrees != PhD (believe me, I know about this).
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@Onegin - you pretty much nailed it there I think.
I'm doing exactly as he said - starting on the sell-side. I think it's a good way to build up my industry understanding, meet and learn from some good people, and prove my worth in a sense.
If you have little background in Finance (full Finance undergraduate, CFA completed), there will likely be significant repetition on the MFin program. Obviously not terrible to be learning things a second time round, especially with the calibre of the teaching staff, but it is worth bearing in mind. With the fundamentals already in place, as is the case with someone like @Onegin, maybe going the more pure-quant route is better. I had little exposure to finance to begin with, and a lot of exposure to data science (from undergrad), so this seemed like the right call for me.
MIT will give you both, with an option to customise it as you wish. CMU will give you all the quant you can ask for, but you may have to figure out some fundamentals on your own. Guess it depends where you put yourself on that wish list.
No wrong decision, IMO.
Side note: good to see some productive discussion here.
I'm doing exactly as he said - starting on the sell-side. I think it's a good way to build up my industry understanding, meet and learn from some good people, and prove my worth in a sense.
If you have little background in Finance (full Finance undergraduate, CFA completed), there will likely be significant repetition on the MFin program. Obviously not terrible to be learning things a second time round, especially with the calibre of the teaching staff, but it is worth bearing in mind. With the fundamentals already in place, as is the case with someone like @Onegin, maybe going the more pure-quant route is better. I had little exposure to finance to begin with, and a lot of exposure to data science (from undergrad), so this seemed like the right call for me.
MIT will give you both, with an option to customise it as you wish. CMU will give you all the quant you can ask for, but you may have to figure out some fundamentals on your own. Guess it depends where you put yourself on that wish list.
No wrong decision, IMO.
Side note: good to see some productive discussion here.
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