I recently graduated from the FinMath program in Dec 2019.
I was very satisfied with the teaching, career services, and professional opportunities provided. The program is majorly taught in Python, and a few classes are in C++.
Things that can be improved: The program can improve on adding more electives, and improve some of the existing ones (namely Machine Learning). Maybe we can have some more advanced classes being offered as electives outside FINM.
Courses: Stand out courses are Option Pricing, Portfolio theory, Quant Strategies. Another advantage is some of the electives offered are cross-listed with the Business school, or Statistics departments, so you can fine tune the course to your requirement.
Project Lab: One of the best experiences to work with companies (HF,AM,Banks) on real problem that they want explore and solve.
Career Services: We had plenty of networking events, company events and alumni dinners to network. I believe the career office is doing a fairly good job.
I just graduated from the MSCF Pittsburgh campus in December 2019. I went straight from my undergraduate study to this program. Having an engineering undergraduate background, I am very grateful to have joined the program. I did not have any experience in finance, financial mathematics and economic study at all. This program well-exposed me to these areas and well-prepared me to find a quant job on the investment banking side. The program's focus on presentation and communication classes help me stand out in my internship program.
I am a current student in Lehigh MFE program. My bachelor degree is finance and I want to develop my technical skills, so that's why I choose this program. The program provides you courses in Finance, Math and Engineering, which probably helped me to improve the skills I need. In addition, our professor helps us to involve in different projects in companies, which not only develops our skills, but also provides us opportunities to network.
I am the 2nd-year MFM student and I will graduate this May. I think it’s time for me to share some of my points of this program.
1. Academic: The top reason I chose this program 2 years ago is that almost all the teachers or instructors are from the industry. Some of them are option, future traders, others are head of quantitative research and analysis, etc. These make our courses both theoretical and practical.
FM5091/ FM5092, we used Matlab (now is changed to Python), C# (GUI is powerful) to price both European options and exotic options. At the end of FM5092, we had to build an option portfolio management GUI. Also, we discussed financial markets for 30 mins each lecture. It’s really nice to know the insight of an experienced exotic option trader.
FM5011/FM5012, the most mathematical courses in the program. We learned measure theory, stochastic calculus, risk neutral measure and portfolio optimization. Math is the thing you never feel it useful until you need it. Thank for these two courses, although I already learned some of them during the undergraduate, I refreshed and consolidated the knowledge.
FM5021/FM5022, courses related to math in financial derivatives. My understanding of derivatives was superficial until I took these courses. Besides, I learned Greeks of options deeply, which is super helpful in hedging and option trading.
FM5031/FM5032, my favorite course in the whole university. The courses are very practical and let you know what quant can do in insurance company, option clearing house and asset management company. The courses are challenging, but it’s really interesting and I learned a lot from the instructors.
Also, we are flexible to choose any courses related to Math, CS, Stats, Econ, Finance and Management from other departments. And if we meet the requirement of specific department, we could get a minor.
2. Career development: I don’t know other programs’ career service, but our program’s is perfect. Laurie Derechin, who’s executive director of our program, is really really helpful! She wants all of us to get the job after graduation, and she spares no effort to help us with resume, interview and life. Based on her effort, our alumni are united. I got a lot of help from our alumni through coffee meeting, FMA meeting, MCFAM seminars and winter workshop. Networking is the key to find a job, and all the alumni are resources.
3. Location: As lots of alumni said before, we are the only MFM program in Minnesota, so we don’t have that many competitions as east coast or west coast. Minnesota is also the best place to live. Don’t be afraid of the cold. I’m from south east of China, and I don’t think the winter here is colder than my hometown…It’s just longer…
That’s all I want to share for now. I’m still trying to get a job now, but I’m not upset since I know I will have a happy ending with all these solid trainings and help from teachers and alumni.
Having worked for a few years after my undergraduate degree, going back for my Masters felt like a huge investment. I was giving up my career, current salary, and on top of that would be paying rent and tuition. I knew that if I was going to switch careers and give up everything I was making now that I would not settle for the best program and school. I was looking to maximize my return.
I quickly realized that CMU's MSCF program offered the best focus on career services while integrating industry relevant education in the coursework. It was evident from the breadth of information and transparency about their employment statistics that I could ensure this switch would work out net positive for me. I'm incredibly happy that I ended up at MSCF and feel that the investment has already paid off two-fold.
I graduated from MSCF in December 2019. The best part about MSCF at CMU is that they value student feedback and make changes to the curriculum every year. MSCF steering committee and the faculty are always in touch with industry practitioners.
I am a current student in Lehigh MFE program and will be graduating in May 2020. After almost two years of studying, I have acquired advanced analytical skills and I am ready to apply what I have learned to working in the industry. The interdisciplinary program allows you to have all the skills needed to be a quant. Besides, there are a lot of resources and network opportunities provided by the program. If you are looking for a program which can help you succeed in quant positions, Lehigh MFE program is definitely one of the best choices!
I am the current student in the MFE program at Lehigh. This program provides a series of interdisciplinary courses in Finance, Engineering and Maths which help me enhance my quantitative skills. It also offers some opportunities to attend financial projects and Quant competition. I would definitely recommend this program.
Bachelor's degree in Engineering from India. Worked as a sell-side analyst for 2 years before going to UChicago MSFM program. Currently working as a desk quant in a bulge bracket.
Did you get admitted to other programs?
Yes, Georgia Tech QCF, Columbia MAFN
Why did you choose this program (over others, if applicable)?
Because of the location, brand- name and some scholarship.
The application process is pretty easy. Submit the transcripts, resumes, application statements with a video.
The courses which liked the most were Options Pricing, Numerical Methods, and Portfolio Theory. Python has become the primary language of the program. Right from September review students are taught Python programming (which I don't see in other MFE programs). I found this to be very helpful in the internship/job interviews. Other programming courses include C++ programming and advanced C++ programming applications.
During the September review, there were some technical interview preparation workshops which I really liked.
There are trading related classes such as Quantitative trading, Applied Algo trading, and Machine Learning in Finance.
Machine learning in finance is a good class to get into the application respective of machine learning in finance and trading. However, this class can be improved a lot.
There are still some required and elective classes that involve heavy math. e.g Stochastic calculus, Fixed income derivates.
Quality of teaching
Option classes are always at the top of academia. Professor Roger Lee always has a unique way of expressing complicated option concepts in simple terms. I enjoyed both his classes despite the heavy math concepts behind.
Prof Mark Hendricks Portfolio Theory is very practical, he follows a business school teaching style where his assignments are interesting case studies by asset management companies.
There are many trading professionals lecturing the classes. E.g Quantitative trading and regression classes are great to get started in trading.
The Machine Learning and Deep Learning classes still need some improvements though.
I really appreciated the video recording of the lectures. I can review any class again.
Overall, the teaching quality is good except for one or two courses. The faculty is a blend of academic professors and industry practitioners
Overall the curriculum is good but needs more elective courses.
This a collaboration between Uchicago Finmath students and companies (hedge funds, banks, asset managers) to work on some real-world research projects. This is the MSFM program's trump card. I know some students (2 or 3 ) who got their internships and full-time roles through project labs but still, it's difficult to convert. But this is really important for those students who come with no prior experience as they can work on lots of interesting projects. Very time consuming but you can learn a lot from it.
According to my understanding, the career service has improved greatly in the past 3 years. I attended many networking events, company information sessions, interview workshops, recruiting events and alumni panels. The Career Development Office helps you a lot in interview preparation. The program is making its best efforts to forge good relationships with employers.
Chicago has a lot of trading firms and exchanges. So it's the ideal place for students trying to get into trading shops. New York anytime is the better location as compared to Chicago. U of C has a competitive advantage for the job market in Chicago. Firms based out of Chicago like interviewing students from this school.
What DON'T you like about the program?
Some of the classes are still pretty theoretical, need to be more practical. Machine Learning course needs improvement.
Suggestions for the program to make it better :
Consider improving Machine Learning and Deep Learning class. Add more electives.
What is your current job status?
I will be working as a desk quant in a bulge bracket.
QCF is an overhyped program poorly managed by Sudheer Chava. Chava just uses this program to charge exorbitant fees relative to the other programs in Georgia Tech. Neither is the program structure good nor the quality of the program. No one gets any teaching assistantship unless you are in the good books of Chava and not by merit, which is very biased.
The MFE program at Lehigh does an excellent job of intertwining the three disciplines of industrial engineering, mathematics, and finance. Each course is extensive and vital to the understanding and preparation of a job in the quant finance industry. All the professors are intelligent, knowledgable, and helpful across most all of the courses. There are many resources amongst the staff involved in the MFE program including advisors and alumni. Lastly, there are many opportunities to get involved in projects on campus directly relating to finance. I would highly recommend this program for those looking for rigorous academia, a friendly campus environment, and strong alumni network.
I graduated from this program in 2015. This program provided me with solid academic foundations, practical knowledge sets, as well as rich alumni connections for me to land a job in the financial services industry upon graduation. I enjoyed my experience at Lehigh and highly recommend this program to anyone who would like to pursue quantitative finance studies at relatively affordable tuition and living costs.
It has been a long time since the last review for this program, so I hope to provide some up-to-date information. Feel free to message me if you have any questions and I’ll try to get back to you. Just refrain from asking me to evaluate your profile, for files from any classes, or for more information on my background.
Things to note:
- Now that there is the 3rd semester option, it is no longer mandatory to take at least 5 courses per semester, thought 4 are required to have full-time status
- The MAFN program offers its own electives in the spring semester
- Career services have improved since the last reviews were posted and the program continues to build up its own individualized career services
Engineering undergrad and IT work.
Did you get admitted to other programs?
Why did you choose this program (over others, if applicable)?
NYC location, university reputation, foundation in mathematics, flexibility in course selection
What alternative sources of info you used to learn more about the program?
QuantNet, Columbia MAFN website
Tell us about the application process at this program
Very straight-forward process outlined on the school website. Typically find out if you’re admitted around late May.
Tell us about the required courses in this program.
MATH GR 5010 – Introduction to the Mathematics of Finance
A thorough, straight-forward overview of various product types and how they are priced, arbitrage, implementation of continuous-time stochastic processes, basic risk management, and basic portfolio construction. Taught by Professor Mikhail Smirnov, sample code/coding for homework is done in MatLab, but what programming language you use for the final project is up to you.
STAT GR 5263 – Statistical Inference/Time-Series Modelling
Course on modelling and inference for random processes. Has a good balance of theory and practical application. Professors vary by semester, but the course content is pretty much the same regardless of the professor you have. Most practical materials, as well as some exam and homework questions, were in R.
STAT GR 5264 – Stochastic Processes - Applications I
Course focused on the foundations of stochastic calculus and continuous-time stochastic processes. This class was the one that seemed to vary the most depending on which professor you had. I had Professor Lars Tyge Nielsen, who provided and taught from his own textbook chapters. His section seemed to focus more on the theory than the other section based on conversations I’ve had, but I found it to be a very thorough and interesting class.
MATH GR 5030 - Numerical Methods in Finance
This course is still the same as it was when the last review for the program went up. It focuses on interpolation, root solving, finite differences, and some simulation depending on if there is enough time left in the semester. It goes into both the theory of the different techniques presented and the implementation of the techniques in Excel using VBA. Professor Tat Sang Fung is still the professor and is still a practitioner.
STAT GR 5265 – Stochastic Methods in Finance
This course focused mainly on the practical implementation of stochastic methods within finance, along with some mathematical and probabilistic tools for analyzing option markets. This includes pricing options in complete and incomplete markets, equivalent martingale measures, utility maximization, and term structure of interest rates. Can be some overlap with STAT GR 5264 in the beginning.
MATH GR 5050 – Mathematical Finance Practitioners Seminar
A seminar series inviting practitioners in the field of quantitative finance and some senior professors from other universities to discuss the work they do. As a result, the content varies from year to year, but it is still a great opportunity to hear from and speak to those in the industry.
Any elective courses in this program you like?
I liked all of the electives I took, but a few that stood out were:
MATH GR 5220 – Quantitative Methods in Investment Management
The whole class centers around a group project where you need to implement a trading strategy that includes accurate, unadjusted point-in-time data, forecasting, portfolio allocation, transaction costs, risk management, and performance analysis/reporting. Beyond this, the project is open-ended in terms of what sectors/products you want to focus on, programming language you want to use, etc. Professor Alexander Greyserman thoroughly explains what he is expecting and common pitfalls in the first few weeks, then proceeds to invite guest speakers consisting mostly of current financial practitioners who provide insight into how the aspects of the project are handled in practice.
IEOR E4732 – Computational Methods in Finance
Professor Ali Hirsa presents applications of a wide variety of computational techniques that are commonly utilized in quantitative finance, including transform (FFT, FrFT for de-noising and pricing), finite difference methods (for PDEs and PIDEs), Monte Carlo simulation, calibration, filtering, and parameter estimation techniques. Final project revolves around taking an existing code for a technique covered in the course and expanding upon it in a meaningful way. Lecture material and the final code are given in Python. I’d recommend taking this course after the Numerical Methods in Finance course, since it contains topics in that course, expands upon them, and adds more methods.
MATH GR 5360 – Mathematical Methods in Financial Analysis
Primarily focused on econophysics, this course provided by Alexei Chekhlov relates current statistical methods used in quantitative finance to different concepts seen in the worlds of engineering and physics. These include position sizing, statistical fluid mechanics/turbulence, Brownian/Random walks, variance ratio tests, memory effects, mean reverting vs push-response functions, and Levy distributions. Final project consists of developing and testing a trading strategy using concepts presented in the course.
Tell us about the quality of teaching
I found the teaching in my MAFN courses to be fantastic, with all of them having a firm grasp of the material, practical knowledge to offer, and willingness to help students outside the classroom if asked. There are also TAs for all classes, all of whom I’ve found to be incredibly helpful and often go out of their way to assist you understand material if you are willing to reach out to them.
Materials used in the program
For most classes, the professor will provide lecture notes/slides. Beyond this, the only outside materials from the required courses are:
MATH GR 5010: Options, Futures, and Other Derivatives by John C. Hull
STAT GR 5264 & 5265: Stochastic Calculus for Finance I and II by Steven Shreve
Most projects involve creating and back-testing trading strategies, with various degrees of complexity depending on the course. The only project from the required courses is the final project in MATH GR 5010, which is a straightforward creation of a trading strategy with some ties to topics covered within the course.
The practical portions of the MATH GR 5030 homework could also feel like projects, where you are to implement the numerical method you are currently learning that week within Excel/VBA.
I see the Columbia Career Services get criticized a lot on here, to the point that many say they are no help at all. I have to respectfully disagree in this regard, as I have been able to take advantage of many great programs like resume help, mock interviews, career fairs, networking/social events, and Lionshare (job website) offered by the University-wide Career Services Center. There are also clubs like the Columbia Quant Group which hold networking and information sessions specifically for quantitative professionals/students.
From the MAFN program, I have been able to participate in employer information sessions and employer open houses aimed specifically towards MAFN students. The MAFN program has also recently hired a full-time career services counselor who provides a listing of job openings, outside employer events like information sessions and hackathons, and holds networking sessions between current students and alumni of the program.
Ultimately, career services and the MAFN program provide plenty of opportunities for you to get your name out there and put your best foot forward in the job hunt.
What do you like about the program?
- Its focus on mathematics and statistics.
- Flexibility in choosing which courses to take.
- Opportunities to meet with companies at info sessions/networking events and to hear from some of the most respected names in the industry through seminars.
- The many resources and opportunities available at Columbia University to explore things outside of your major requirements/courses (ie. I was able to participate in research).
- Projects within classes that have clear practical purposes and allow for flexibility in approach.
What DON’T you like about the program?
- Courses from the statistics department heavily favor R programming over other languages
- Though the number of electives offered in-house by the MAFN program has improved, it still has its limitations.
- The process to register for courses from the Business and Engineering School is not the easiest to find and you are limited to only one course from each in any given semester. You basically have to fill out a Google form for each school where you list 3 courses you’d like to take and hope your #1 choice has an open spot after the 2 weeks of registration are up.