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University of Michigan - MS in Quantitative Finance and Risk Management
3-term Master of Science degree administered by the Departments of Mathematics and Statistics
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Introduction:
I write this review from the perspective of a student who has a balanced mathematics and statistics background whose goals were to get a practical education in quantitative finance to use towards a career in the industry.
1. Curriculum: The core curriculum of financial mathematics (4), probability (2), and statistics (2) may seem quite reasonable at first glance, but the material is presented in such a way that is often overly theoretical and more complicated than it needs to be. Additionally, the applicability of such knowledge to most graduates of this program in their future careers is suspect at best.
The courses of most interest personally were the statistics and probability courses. Overall, the quality of instruction there is somewhat decent and their application in general for students going into software development or data science may not be entirely nil. However, based on my collection of experiences, the quality of course depends greatly on the instructor and utilization of things such as office hours is 100% a must if you want to have a ghost of a chance at doing well in classes.
As for financial mathematics, the coursework is deeply theoretical and often presented in ways that are difficult to digest. The pacing of the coursework is also irregular and exponential in its difficulty. The impression I get in common with multiple cohorts of students from this program, both old and new, is that it is almost entirely impossible to succeed without a collaborative effort. Although it had been mentioned that the course material in these classes may be useful for a quant interview, I would say that point is moot, because studying directly for interviews using things such as ‘the green book’ and other similar study guides is almost surely more effective than trying to do so through studying the course material.
As a whole, the pacing of the coursework is grueling, leaving students little to no time to balance their studies with preparation to enter the industry (a goal to which probably over 95% of the student body aspires to).
Lastly, as a rebuttal to some previous reviews, I would like to say that the faculty teaching most of the classes are most certainly not uncaring about the students – they recognize the difficulty of the material for most and can be of great help if a student just asks for it.
Suggestions:
1. Reconsider the pacing of the coursework, both as a whole and how it is taught in class. The current pace often catches students off guard, sending the students into despair by mid-semester. I would suggest offering alternative settings of curriculum to students and presenting plans for graduation in 1.5 years, 2 years, 2.5 years and 3 years based on how they want to handle their course load.
2. Add flexibility to the graduation requirements for this program. For students who desire to go towards a PHD in financial mathematics, the current coursework may be appropriate, but for most who just want to get a job in the industry a more practical touch may be required. I would suggest allowing multiple similar courses to fulfil the same requirements for topics that may be pain-points for students. Additionally, there should be options to forego financial mathematics in detail, by instead taking courses related to programming, and software development as these appear to be in high demand in the current job marketplace (for quants and otherwise).
2a. Alternative (experimental) suggestion, divide some core courses into half-semester segments, with the first covering material at a high-level with a focus on conceptual understanding, and the second on details, with the second swappable with a finance/statistics/programming course with equal credit. This would allow students to ‘sample’ the ideas before fully committing to it. Then arrange the remainder of the curriculum around it, creating a unique customizable degree that covers some elements in depth, but the more extraneous ones in breadth.
2. Student Placement: Student placement is poor from this program, there is no other way to say it. To any potential candidates hoping to land a lofty job at a top-tier firm in the US after graduation from this program, I wouldn’t say it is impossible, but it would be a rare occurrence and based almost solely on your own skill and innate aptitude. The program will neither be a positive nor a negative influence on this. It is possibly equally or more likely to get into a good quant job from an undergrad degree than from this program.
Suggestions:
1. Realistically, there are not enough positions in high-tier quant firms for all students of all quant programs to go there. However, there are plenty positions in software development and data science that are looking for students with a good statistics background and problem-solving skills.
2. Due to the high-proportion (90%+) of Chinese international students in the program, it may be appropriate to offer more options to build communication and interview prep skills in the US market.
3. Building off of some suggestions about the curriculum, it may be time to rebrand this degree (again), so as to appeal to more diverse types of employers and to accommodate career decisions that students might make mid-program. Quantitative finance is a small subset of the financial industry as a whole, and the esoteric knowledge requisite for it absolutely useless to jobs outside of the space. I believe its better to give the students options (through course flexibility or otherwise), so that they may be both, 1. Satisfied with the applicability education they received from the program and 2. Land a job where they feel their education will serve them well in the future, because in the most realistic sense of things, 90+% of the graduates from this program will not be going to quant-finance.
3. Student Life: The campus of Ann Arbor is large, with a wide array of things to do in ready walking or biking distance. However, this potentially fun and exciting location may as well be seen through a glass window from a padded room for the students in the quant program. The demanding curriculum essentially keeps students chained to their desks, furiously studying for a vague hope of survival. I’ve known more than several who were stressed to the point of not eating for fear of not being able to keep up with the coursework. Even then, they were hardly able to break the median score when examinations came due. In short, to say the student life is anything less than excruciation would be an understatement. In the past, the kind student coordinator would have been able to alleviate some of this, helping students manage their stress or arranging various avenues for students to manage their course loads, but they have since departed from the program, so new students can expect no such reprieve.
Suggestions:
1. Student life will likely be improved by doing the aforementioned changes to curriculum and placement. Also, get a new coordinator – at least as good as the previous one who was excellent.
Conclusion/Impressions:
If you are a PHD hopeful, you may find this program useful and the right step towards your next adventure in academia. I was lucky enough to end up doing something unrelated to the degree that I love, but, if you just want to get an education that you feel will serve you well in your next job, look elsewhere, there are plenty of other places more deserving of your money.
I write this review from the perspective of a student who has a balanced mathematics and statistics background whose goals were to get a practical education in quantitative finance to use towards a career in the industry.
1. Curriculum: The core curriculum of financial mathematics (4), probability (2), and statistics (2) may seem quite reasonable at first glance, but the material is presented in such a way that is often overly theoretical and more complicated than it needs to be. Additionally, the applicability of such knowledge to most graduates of this program in their future careers is suspect at best.
The courses of most interest personally were the statistics and probability courses. Overall, the quality of instruction there is somewhat decent and their application in general for students going into software development or data science may not be entirely nil. However, based on my collection of experiences, the quality of course depends greatly on the instructor and utilization of things such as office hours is 100% a must if you want to have a ghost of a chance at doing well in classes.
As for financial mathematics, the coursework is deeply theoretical and often presented in ways that are difficult to digest. The pacing of the coursework is also irregular and exponential in its difficulty. The impression I get in common with multiple cohorts of students from this program, both old and new, is that it is almost entirely impossible to succeed without a collaborative effort. Although it had been mentioned that the course material in these classes may be useful for a quant interview, I would say that point is moot, because studying directly for interviews using things such as ‘the green book’ and other similar study guides is almost surely more effective than trying to do so through studying the course material.
As a whole, the pacing of the coursework is grueling, leaving students little to no time to balance their studies with preparation to enter the industry (a goal to which probably over 95% of the student body aspires to).
Lastly, as a rebuttal to some previous reviews, I would like to say that the faculty teaching most of the classes are most certainly not uncaring about the students – they recognize the difficulty of the material for most and can be of great help if a student just asks for it.
Suggestions:
1. Reconsider the pacing of the coursework, both as a whole and how it is taught in class. The current pace often catches students off guard, sending the students into despair by mid-semester. I would suggest offering alternative settings of curriculum to students and presenting plans for graduation in 1.5 years, 2 years, 2.5 years and 3 years based on how they want to handle their course load.
2. Add flexibility to the graduation requirements for this program. For students who desire to go towards a PHD in financial mathematics, the current coursework may be appropriate, but for most who just want to get a job in the industry a more practical touch may be required. I would suggest allowing multiple similar courses to fulfil the same requirements for topics that may be pain-points for students. Additionally, there should be options to forego financial mathematics in detail, by instead taking courses related to programming, and software development as these appear to be in high demand in the current job marketplace (for quants and otherwise).
2a. Alternative (experimental) suggestion, divide some core courses into half-semester segments, with the first covering material at a high-level with a focus on conceptual understanding, and the second on details, with the second swappable with a finance/statistics/programming course with equal credit. This would allow students to ‘sample’ the ideas before fully committing to it. Then arrange the remainder of the curriculum around it, creating a unique customizable degree that covers some elements in depth, but the more extraneous ones in breadth.
2. Student Placement: Student placement is poor from this program, there is no other way to say it. To any potential candidates hoping to land a lofty job at a top-tier firm in the US after graduation from this program, I wouldn’t say it is impossible, but it would be a rare occurrence and based almost solely on your own skill and innate aptitude. The program will neither be a positive nor a negative influence on this. It is possibly equally or more likely to get into a good quant job from an undergrad degree than from this program.
Suggestions:
1. Realistically, there are not enough positions in high-tier quant firms for all students of all quant programs to go there. However, there are plenty positions in software development and data science that are looking for students with a good statistics background and problem-solving skills.
2. Due to the high-proportion (90%+) of Chinese international students in the program, it may be appropriate to offer more options to build communication and interview prep skills in the US market.
3. Building off of some suggestions about the curriculum, it may be time to rebrand this degree (again), so as to appeal to more diverse types of employers and to accommodate career decisions that students might make mid-program. Quantitative finance is a small subset of the financial industry as a whole, and the esoteric knowledge requisite for it absolutely useless to jobs outside of the space. I believe its better to give the students options (through course flexibility or otherwise), so that they may be both, 1. Satisfied with the applicability education they received from the program and 2. Land a job where they feel their education will serve them well in the future, because in the most realistic sense of things, 90+% of the graduates from this program will not be going to quant-finance.
3. Student Life: The campus of Ann Arbor is large, with a wide array of things to do in ready walking or biking distance. However, this potentially fun and exciting location may as well be seen through a glass window from a padded room for the students in the quant program. The demanding curriculum essentially keeps students chained to their desks, furiously studying for a vague hope of survival. I’ve known more than several who were stressed to the point of not eating for fear of not being able to keep up with the coursework. Even then, they were hardly able to break the median score when examinations came due. In short, to say the student life is anything less than excruciation would be an understatement. In the past, the kind student coordinator would have been able to alleviate some of this, helping students manage their stress or arranging various avenues for students to manage their course loads, but they have since departed from the program, so new students can expect no such reprieve.
Suggestions:
1. Student life will likely be improved by doing the aforementioned changes to curriculum and placement. Also, get a new coordinator – at least as good as the previous one who was excellent.
Conclusion/Impressions:
If you are a PHD hopeful, you may find this program useful and the right step towards your next adventure in academia. I was lucky enough to end up doing something unrelated to the degree that I love, but, if you just want to get an education that you feel will serve you well in your next job, look elsewhere, there are plenty of other places more deserving of your money.
For those (probably vast majority of) students who don't desire to pursue a phd degree in financial mathematics or find a job in quant research, the QFRM Program at UM may not be a good choice for you. There are several reasons:
1. Curriculum.
The required curriculum consists of 6 math courses and 2 statistics courses, which is solid, tough, but WAY TOO theoretical, lacking real application, especially for core math courses. I have to admit that solid mathematical background is important for quantitative finance field, but how the theoretical knowledge can be connected to real finance world is also crucial. However, seldom do core math courses provide students with application or practice such as hand-on financial engineering related projects to see how the theoretical knowledge learned in class can be applied to the real industry; the only thing that this program teaches students is math, math, and math. Besides, the curriculum is inflexible in that students should take courses in a fixed order (4 required in the first semester, 3 in the second, and 1 in the third), which prevents students from learning as much as what they want in the first year. During the 3rd semester, students can choose enough electives towards their interests such as CS, DS, Stats, Econ, Finance, but as mentioned above how applicable knowledge and skills learned in these electives can be applied to the theretical knowledge learning in math class is what a quant finance program should really teach students.
2. Connection with real industry.
This program lacks opporunities for students to connect or network with professionals in the real industry. There is no chance to work with local corporates for finishing projects in the real industry like some of the other programs have. The program do have some academic seminars, but all of them are about theretical math, instead of real talk with real quants who can share working experiences as a quant, or introducing different topics in the real finance world. Of course, beginning from winter 2020, the program opens a new course called "Machine Learning for Finance" only for students in this program, and the instructor is a real quant. However, the structure of this course is a little bit messy, and the instructor can hardly explain materials clearly, and he never pay attention to how students learn, forgetting about the advantage of having a real quant as an instructor. But anyway the program is making progress that is a positive signal.
3. The care about the students.
Neither the program director nor your academic advisor care about how students learn, or students' job placement, or what difficulties students encounter. The only person who really cares about students is the program cordinator, who is a super enthusiastic lady, negotiating students with any kind of issues, updating job posting information, scheduling mock interview, helping current students network with alumni of this program. But unfortunately effort from one person in the program is not enough.
Overall this program is not recommended for you unless you want to apply for phd in mathematical finance, work as a quant researcher, or you really really love math.
1. Curriculum.
The required curriculum consists of 6 math courses and 2 statistics courses, which is solid, tough, but WAY TOO theoretical, lacking real application, especially for core math courses. I have to admit that solid mathematical background is important for quantitative finance field, but how the theoretical knowledge can be connected to real finance world is also crucial. However, seldom do core math courses provide students with application or practice such as hand-on financial engineering related projects to see how the theoretical knowledge learned in class can be applied to the real industry; the only thing that this program teaches students is math, math, and math. Besides, the curriculum is inflexible in that students should take courses in a fixed order (4 required in the first semester, 3 in the second, and 1 in the third), which prevents students from learning as much as what they want in the first year. During the 3rd semester, students can choose enough electives towards their interests such as CS, DS, Stats, Econ, Finance, but as mentioned above how applicable knowledge and skills learned in these electives can be applied to the theretical knowledge learning in math class is what a quant finance program should really teach students.
2. Connection with real industry.
This program lacks opporunities for students to connect or network with professionals in the real industry. There is no chance to work with local corporates for finishing projects in the real industry like some of the other programs have. The program do have some academic seminars, but all of them are about theretical math, instead of real talk with real quants who can share working experiences as a quant, or introducing different topics in the real finance world. Of course, beginning from winter 2020, the program opens a new course called "Machine Learning for Finance" only for students in this program, and the instructor is a real quant. However, the structure of this course is a little bit messy, and the instructor can hardly explain materials clearly, and he never pay attention to how students learn, forgetting about the advantage of having a real quant as an instructor. But anyway the program is making progress that is a positive signal.
3. The care about the students.
Neither the program director nor your academic advisor care about how students learn, or students' job placement, or what difficulties students encounter. The only person who really cares about students is the program cordinator, who is a super enthusiastic lady, negotiating students with any kind of issues, updating job posting information, scheduling mock interview, helping current students network with alumni of this program. But unfortunately effort from one person in the program is not enough.
Overall this program is not recommended for you unless you want to apply for phd in mathematical finance, work as a quant researcher, or you really really love math.