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I have been working at a top hedge fund for few years. I'm applying for few MFE/MFin programs(UCB MFE, Princeton MFin, Stanford MCF and Columbia Fin and Econ, CMU MSCF) this year. On my own opinion, this program is on my top choice along with Princeton, Stanford is second and UCB/CMU are least choice.
I personally interviewed ~100 candidates and had a built a network with PMs\Quants in different firm at different level. I would like to share my own thoughts about this program and why I think it is indeed a good one. Hopefully can provide some reference to others.
1. Career services: When evaluating these MFE/MFin programs, people tend to put a lot of emphasis on the placement stats, what percentage of students get job, what's the avg salary etc.. New grads or those with not much experience put even more emphasis on placement or career service, which is totally understandable and I had the exactly same thought process when I graduated few years ago. I think there are two points here that worth you a bit of time to consider:
1a. For large, well-established programs with outstanding career service(UCB, CMU, Columbia MFE, Cornell, NYU Mfin, NYU MFE) the majority of placement stats are contributed by banks. In my own opinion, you get a good chance of getting into quant position at banks as long as you have solid practice in the quant interview books and leetcode, with/without the need for "career service". And the MFE training also doesn't matter much for banks. You can get into the door with any quantitative masters like CS/Math/Stat. At the end of day, it is all about solving technical questions during interview and applying for more banks. So for top program, I personally won't give too much weight to stats like 100% placement rate VS 90% placement rate. The metrics worth more is "how many students joining buy side funds/prop trading after graduation". On the perspective, UCB, Princeton, CMU probably have better placement "quality" than Columbia MFE and Cornell. NYU MathFin probably > NYU Tandon. On the other hand, the MS FinEcon program place several candidates at good buy side firm every year. Considering the smaller cohort size, the percentage of students ended up in buy side is on par with UCB/CMU/Princeton. From the pure outcome stat, I don't think this particular program is at disadvantage.
1b. What are the real career "service" we are discussing about? If we are talking about resume editing/proof reading, resume book, mock interview, tbh, the "dedicated" career service staff at UCB/CMU/Columbia don't have any superstar background themselves. For things like resume/mock interview/interview preparation, a friend working in the industry and making the interview decisions is a better choice. If we are talking about program director aggressive place their students using their own personal connection that is a different story. Linda(UCB), Dan(Baruch), Peter(NYU) all had strong connections to senior level at different firms and had placed students using their connections. But I don't think they would do that for every one in the program simply because of the cohort size. So, if your other option is a program with a very dedicated director and you believe he/she would help you in your specific case, then that is huge benefit and you should take advantage of it. Otherwise, the "bad" career service at Columbia Fin Econ isn't a big deal because those career services provide at other MFE programs isn;t worth that much as you thought.
2. Curriculum: The curriculum design plays an important role in my own program selection/evaluation. I can explain why it is important in terms of employment and career development.
2a. Quant is relative small sub field in finance, and then it being divided into even smaller types risk quant, validation quant, desk quant, quant developer, p-quant, q-quant etc... Logically, all MFE programs have to be "generic" so that their students are prepared for "all" types of quant role after graduation. As a result, students take courses in Stochastic Calculus, Derivate Pricing, Econometrics and Portfolio Theory. The said fact is people working in Option Market Making my find the later two useless and people working in Equity Systematic Trading may find first two useless. The well-designed fixed curriculum is a protection for students with little to no understanding of quant job markets but it is a burden for students who has a clear targeted career path. To that extent, program with larger flexibility Princeton/Columbia FinEcon/Stanford are more suitable for me than UCB/CMU/NYU. Also, the relatively tradition training at a FinEcon with emphasis on Econometrics, Time Series, Asset Pricing is more suitable for people interested in buy side equity alpha research or quantitative asset management.
2b. Master degree is a bit embarrassing in quant career. At most firm, the ability to conduct independent research is them most important requirement of a quant researcher. That's why they hire a lot of math/stat/econ Phds. Most MFE students are end up doing "implementation" work even their job title may be quant researcher (Of course, this is not definite conclusion, there are a lot of outstand MFEs doing research work but those are not the general cases). To that extent, courses with more theoretical depth, the opportunity of doing quantitative research and the possibility of having a published master thesis can add comparable advantage and better position master students in the long run. Princeton and the Columbia F&E have a big Plus here!
2c. A lot of programs advertise the amount of faculty coming from industrial and you can learn "real world" experience in their programs. Those are great but just don't over estimate the benefit you can get. Trust me, you get far more opportunities to work with and learn from MDs at an bank or fund than work with outstanding academic faculties. Most people probably have access to professors while they will be working with their colleagues(senior people with experience and those MDs, head of research etc..). In addition, the true "real world" experience cannot be "learned" in classroom not matter who is teaching the class. There is a preferred flavor in terms of more theoretical or more applied way of training but I don't think there is a huge advantage of one over another.
3. Brand name and connections: I dis-agree with a belief shared by some students that the brand name doesn't matter after your first job. It may not matter much in terms of what major/program you studied but the school does matter and it plays an important role in the long run as well. When we interview candidates, a good portion of them are typical candidates jumping from sell-side to buy-side, we do consider their schools. We probably won't reject strong candidate coming from tier 2 schools and we won't tell the candidate your degree from Cal Tech is great. Furthermore, alumni community is very important in the long run. PMs tend to give opportunists and trust their alumni if everything else stays same (human nature). To that extent, a program in B-School is more favorable than a program in Eng school, considering the huge MBA program size and the reputation in finance industry(Yeah, I'm talking about Columbia). A program with smaller cohort size (Princeton) may not as good as a strong program with larger cohort size (CMU).
4. Few thoughts regarding Finance PhD:
a. Finance PhD is highly quantitative. Core PhD trainings micro/macro/econometrics are solid theoretical math. For those fields, finance PhDs go through similar depth as Math PhDs and definitely much more technical than MFE training.
b. Industry does hire finance PhDs. The head of GQS at Citadel holds finance PhD degree as well as a lot of other PMs at different funds. PMs on systematic equity space cares more about finance intuitions and skills like econometrics/time-series than machine learning/deep learning. ML/DL are becoming popular but they are not as widely used in production as some people thought.
c. People see more Math/Stat/Econ/Physic PhDs could because 1) On avg, it is less competitive for B-school PhDs to get a job in academia than other fields. And B-school faculty position pay much higher than Math/Stat/Econ/Physics (some may got same level of starting comp as hedge funds). For same reason, people may not see many CS PhDs in finance because they can find faculty positions or better positions in tech. 2) The supply of Finance PhDs are much smaller than science PhDs. Typical B-school admit <10 finance PhD each year whereas a applied math/statistics program may admit ~20 students each year.
Anyway, thanks for reading this verbose post up until now. I think the master in financial economics program at Columbia is a strong one for its PhD level courses, the opportunities to do research with GSB faculty, the flexibility to take MBA courses(never offered in another other MFE/MFfin) and courses from other Columbia school(including IEOR) and the long term network of GSB. In addition, the emphasis on quantitative finance training(econometrics, micro/macro, time series) are highly desired skills for buy-side equity space. For people with less interest in MM or derivative but more on equity strategies or portfolio management, they can safe the time spends on Stochastic Calculus/C++(most likely not need for a quant research or PM). As you may tell, Princeton's MFin is very competitive for the above criteria (1 stoc cal core but a lot electives).
I personally interviewed ~100 candidates and had a built a network with PMs\Quants in different firm at different level. I would like to share my own thoughts about this program and why I think it is indeed a good one. Hopefully can provide some reference to others.
1. Career services: When evaluating these MFE/MFin programs, people tend to put a lot of emphasis on the placement stats, what percentage of students get job, what's the avg salary etc.. New grads or those with not much experience put even more emphasis on placement or career service, which is totally understandable and I had the exactly same thought process when I graduated few years ago. I think there are two points here that worth you a bit of time to consider:
1a. For large, well-established programs with outstanding career service(UCB, CMU, Columbia MFE, Cornell, NYU Mfin, NYU MFE) the majority of placement stats are contributed by banks. In my own opinion, you get a good chance of getting into quant position at banks as long as you have solid practice in the quant interview books and leetcode, with/without the need for "career service". And the MFE training also doesn't matter much for banks. You can get into the door with any quantitative masters like CS/Math/Stat. At the end of day, it is all about solving technical questions during interview and applying for more banks. So for top program, I personally won't give too much weight to stats like 100% placement rate VS 90% placement rate. The metrics worth more is "how many students joining buy side funds/prop trading after graduation". On the perspective, UCB, Princeton, CMU probably have better placement "quality" than Columbia MFE and Cornell. NYU MathFin probably > NYU Tandon. On the other hand, the MS FinEcon program place several candidates at good buy side firm every year. Considering the smaller cohort size, the percentage of students ended up in buy side is on par with UCB/CMU/Princeton. From the pure outcome stat, I don't think this particular program is at disadvantage.
1b. What are the real career "service" we are discussing about? If we are talking about resume editing/proof reading, resume book, mock interview, tbh, the "dedicated" career service staff at UCB/CMU/Columbia don't have any superstar background themselves. For things like resume/mock interview/interview preparation, a friend working in the industry and making the interview decisions is a better choice. If we are talking about program director aggressive place their students using their own personal connection that is a different story. Linda(UCB), Dan(Baruch), Peter(NYU) all had strong connections to senior level at different firms and had placed students using their connections. But I don't think they would do that for every one in the program simply because of the cohort size. So, if your other option is a program with a very dedicated director and you believe he/she would help you in your specific case, then that is huge benefit and you should take advantage of it. Otherwise, the "bad" career service at Columbia Fin Econ isn't a big deal because those career services provide at other MFE programs isn;t worth that much as you thought.
2. Curriculum: The curriculum design plays an important role in my own program selection/evaluation. I can explain why it is important in terms of employment and career development.
2a. Quant is relative small sub field in finance, and then it being divided into even smaller types risk quant, validation quant, desk quant, quant developer, p-quant, q-quant etc... Logically, all MFE programs have to be "generic" so that their students are prepared for "all" types of quant role after graduation. As a result, students take courses in Stochastic Calculus, Derivate Pricing, Econometrics and Portfolio Theory. The said fact is people working in Option Market Making my find the later two useless and people working in Equity Systematic Trading may find first two useless. The well-designed fixed curriculum is a protection for students with little to no understanding of quant job markets but it is a burden for students who has a clear targeted career path. To that extent, program with larger flexibility Princeton/Columbia FinEcon/Stanford are more suitable for me than UCB/CMU/NYU. Also, the relatively tradition training at a FinEcon with emphasis on Econometrics, Time Series, Asset Pricing is more suitable for people interested in buy side equity alpha research or quantitative asset management.
2b. Master degree is a bit embarrassing in quant career. At most firm, the ability to conduct independent research is them most important requirement of a quant researcher. That's why they hire a lot of math/stat/econ Phds. Most MFE students are end up doing "implementation" work even their job title may be quant researcher (Of course, this is not definite conclusion, there are a lot of outstand MFEs doing research work but those are not the general cases). To that extent, courses with more theoretical depth, the opportunity of doing quantitative research and the possibility of having a published master thesis can add comparable advantage and better position master students in the long run. Princeton and the Columbia F&E have a big Plus here!
2c. A lot of programs advertise the amount of faculty coming from industrial and you can learn "real world" experience in their programs. Those are great but just don't over estimate the benefit you can get. Trust me, you get far more opportunities to work with and learn from MDs at an bank or fund than work with outstanding academic faculties. Most people probably have access to professors while they will be working with their colleagues(senior people with experience and those MDs, head of research etc..). In addition, the true "real world" experience cannot be "learned" in classroom not matter who is teaching the class. There is a preferred flavor in terms of more theoretical or more applied way of training but I don't think there is a huge advantage of one over another.
3. Brand name and connections: I dis-agree with a belief shared by some students that the brand name doesn't matter after your first job. It may not matter much in terms of what major/program you studied but the school does matter and it plays an important role in the long run as well. When we interview candidates, a good portion of them are typical candidates jumping from sell-side to buy-side, we do consider their schools. We probably won't reject strong candidate coming from tier 2 schools and we won't tell the candidate your degree from Cal Tech is great. Furthermore, alumni community is very important in the long run. PMs tend to give opportunists and trust their alumni if everything else stays same (human nature). To that extent, a program in B-School is more favorable than a program in Eng school, considering the huge MBA program size and the reputation in finance industry(Yeah, I'm talking about Columbia). A program with smaller cohort size (Princeton) may not as good as a strong program with larger cohort size (CMU).
4. Few thoughts regarding Finance PhD:
a. Finance PhD is highly quantitative. Core PhD trainings micro/macro/econometrics are solid theoretical math. For those fields, finance PhDs go through similar depth as Math PhDs and definitely much more technical than MFE training.
b. Industry does hire finance PhDs. The head of GQS at Citadel holds finance PhD degree as well as a lot of other PMs at different funds. PMs on systematic equity space cares more about finance intuitions and skills like econometrics/time-series than machine learning/deep learning. ML/DL are becoming popular but they are not as widely used in production as some people thought.
c. People see more Math/Stat/Econ/Physic PhDs could because 1) On avg, it is less competitive for B-school PhDs to get a job in academia than other fields. And B-school faculty position pay much higher than Math/Stat/Econ/Physics (some may got same level of starting comp as hedge funds). For same reason, people may not see many CS PhDs in finance because they can find faculty positions or better positions in tech. 2) The supply of Finance PhDs are much smaller than science PhDs. Typical B-school admit <10 finance PhD each year whereas a applied math/statistics program may admit ~20 students each year.
Anyway, thanks for reading this verbose post up until now. I think the master in financial economics program at Columbia is a strong one for its PhD level courses, the opportunities to do research with GSB faculty, the flexibility to take MBA courses(never offered in another other MFE/MFfin) and courses from other Columbia school(including IEOR) and the long term network of GSB. In addition, the emphasis on quantitative finance training(econometrics, micro/macro, time series) are highly desired skills for buy-side equity space. For people with less interest in MM or derivative but more on equity strategies or portfolio management, they can safe the time spends on Stochastic Calculus/C++(most likely not need for a quant research or PM). As you may tell, Princeton's MFin is very competitive for the above criteria (1 stoc cal core but a lot electives).