This review was submitted on 12/22/2009 at 21:53:39 by Chris Dolan who studied full-time in the program from 9/2008-12/2009*
Can you tell us a bit about your background?
I had majored in math as an undergrad, and taken several graduate level courses. I knew that I wanted to do something heavily quantitative for a career. After college, I worked for a year at a European bank with a small US office doing municipal finance, which was a pretty good way to get some basic exposure to the financial markets.
Did you get admitted to other programs?
I was admitted to NYU, CMU, Baruch, Columbia (FE), and Rutgers.
Why did you choose this program (over others, if applicable)?
I liked the three semester setup, with the opportunity to do an internship. Moreover, I figured that being in New York would be the best bet for landing a job on Wall Street. I also felt like most NYU courses were taught by really famous practitioners, and that it was a great opportunity to get to know these people and learn first-hand from them.
Tell us about the application process at this program
The application process was pretty uneventful. I had a deadline to respond to a graduate school in February, and they provided me with an expedited response to meet that deadline.
On a scale of 1-10, how would you grade the accessibility of the faculty and staff?
5
Programs like Baruch MFE, UCB MFE have refresher courses for incoming students. Does this program offer such courses? How useful was it?
N/A
On a scale of 0-10, how would you grade the usefulness of these refresher courses?
Not offered
Tell us about the courses selection in this program. Any special courses you like?
The courses cover a variety of topics. There are the traditional sell-side courses – mainly, a two-semester sequence in the pricing of derivatives: Derivative Securities and Continuous Time Finance, as well as more mathematical courses like Stochastic Calculus and PDE in Finance.
There is also a strong buy-side focus that most programs don’t have (I think), with four courses – Risk and Portfolio Management, Active Portfolio Management, Algorithmic Trading and Quantitative Strategies, and Time Series and Statistical Arbitrage – the latter three are electives.
There are three required computing courses. There are also a number of electives, including risk management, interest rate and credit modeling, mortgage backed securities, energy trading. Most students are able to take 4 electives over 3 semesters.
On a scale of 1-10, how would you grade the flexibility of the curriculum?
7
Tell us about the quality of teaching
Almost all of the courses are taught by practitioners. As such, they are a bit less accessible than tenured faculty would be, although most of them make time for office hours and are quite responsive to emails. In the classroom, some teachers left something to be desired, while others were rather inspired lecturers. Overall, I was pretty happy with the teaching.
As an aside, we could choose a number of electives, and some courses had multiple sections. I tended to sit in on classes at the beginning, and make my course selections based on which professors I liked. So I didn’t have any experiences with really bad teaching.
All the courses have a TA – usually a PhD student, sometime a 3rd semester student in the math finance program. Most of them weren’t especially helpful.
On a scale of 1-10, how would you grade the quality of teaching?
7
Materials used in the program
There are some standard texts used in the first semester – Hull, Shreve (Vol II). Most courses, though, use instructor’s notes.
On a scale of 1-10, how would you grade the practicality of the curriculum?
8
Programming component of the program
There are three computing courses in the program – Computing in Finance, Scientific Computing, and Computational Methods in Finance. Many students place out of scientific computing.
Computing in Finance is the object oriented programming course, done entirely in C++.
There were regular assignments as well as a very large end-of-semester project, building an object oriented pricing library.
Scientific Computing was more of an introduction to numerical analysis. It involved weekly assignment in C++ and Matlab.
Computational Methods in Finace was the hardcore numerical analysis course. We implemented very recent numerical techniques and models – papers that were written in the last few year. There were three large assignments, all in C++.
Many other classes had computational assignments. These usually involved MATLAB or VBA, although at least one class used R. Risk and Portfolio management involved using a lot of MATLAB.
When you finish the program you will definitely have solid C++ aptitude (although you won’t qualify as an expert), and you will be very good at MATLAB.
Projects
In the first semester, in the mandatory computing class, there was a group project involving building an object-oriented pricing library for a variety of projects.
A number of other courses involved doing an end-of-semester project instead of a final, or in addition to a final exam. An example of this was the Interest Rate and Credit Models class, where the final project was to build a Monte Carlo simulator for synthetic CDOs, or Time Series and Statistical Arbitrage, where we had to get market data, clean it, and develop a trading strategy using it.
Finally, there is a required course called project and presentation, where students work on a semester-long project – comparable to a master’s thesis – with an advisor and some team members. We are able to choose our own projects and have to find our own advisers. Most students found advisers who teach in the program, although some found them from their internships or other networking. The projects are varied – some are very computational or data intensive, while others are rather theoretical.
Career service
The program is three semesters long – so students are encouraged/required to do an internship over the summer. Finding an internship was a challenge, mostly due to the uncertain economic climate. Recruiting for internships, though, picked up a lot in late April and early May. Finding a full-time job was much easier.
We have access to all of NYU’s career services (except for the business school MBA recruiting), which are pretty comprehensive. A lot of companies recruit on campus for analyst positions, and they are more than happy to interview MS students. Some post jobs through NYU’s career portal. A handful of places come on campus specifically to recruit Math Finance finance students. The program is well connected, and we frequently get notified of job openings at various places which we can apply to, as well as sending around a resume book from which some students have gotten jobs and internships.
The program runs a career workshop that meets every week for a several hours during the first few weeks of the program. In it, he goes over making a resume, networking, and preparing for quantitative and behavioral interviews.
On a scale of 1-10, how would you grade the career service for internship and full-time job?
9
Can you comment on the social interaction between students of different ethnics, nationalities in the program?
I can only speak for my graduating class – pretty much everyone got along. A couple of the chinese students tended to keep to themselves, but the rest of them were very social with everyone. As far as the handful students doing their own thing, some were older, some just chose not to be too social. Everyone else was very social with one another, and tended to work together on various projects/assignments.
What do you like about the program?
I think the opportunity to work with and learn really high-quality practitioners is a great aspect of the program. It’s not every day that you get to be taught by people like Peter Carr, Bruno Dupire, Robert Almgren, just to name a few. Most of them are very approachable, and they all supervised student projects in the third semester.
There is also an effort to expose students to a lot of cutting edge stuff – particularly in the continuous time finance course, and in the computational methods course. Useful stuff to learn in general, and also useful to discuss in job interviews.
I feel like the career support was very good for an MFE/MSMF program. It wasn’t business school recruiting, but there was a definite effort to provide professional career services. We were made aware of a lot of job opportunities, and we also had access to the NYU career center.
The approach in the program is very balanced, I feel. There is an effort to teach finance, mathematics and computing. In spite of it being housed in a math department, there is definitely NOT an axiomatic approach to teaching finance. In teaching material, there is definitely a philosophical emphasis on understanding the weaknesses of one’s assumptions and model risk.
On a scale of 1-10, how would you grade the value of the program for the price tag?
9

continued in http://www.quantnet.com/review-nyu-msmf-program