Columbia University Financial Economics (MSFE)

My Background:
-Engineering undergrad from a top school. I joined the program with 3 years experience in O&G sector.

Other Admits:
MIT MFin
UChicago MSFM
UCLA MFE

Overall, I highly recommend this program, especially for those with non-traditional finance backgrounds. The program's flexibility and two-year format allowed me to explore a variety of coursework across departments while still specializing in my interests.

One of the program's key strengths of the program is its requirement of a minimum of 10 out of 16 classes to be at the PhD level, providing training in finance comparable to that of a finance PhD student. However, unlike PhD students, the MSFE program requires students to take at least 16 courses, arguably providing a more rigorous and well rounded training than a second-year finance PhD student. Additionally, the program's high flexibility and availability of exceptional coursework in financial mathematics, financial engineering and MBA at Columbia gives the program an unparalleled competitive edge over others.

There are some areas for improvement, however. Firstly, the career services team is largely ignorant about the quantitative finance industry, and students must often source their own career opportunities. However, the professors are really helpful and the new program director is working to improve things on this front, with recent announcements of resumed in-semester internships opportunity with Bank of America(interrupted due to Covid) and talks scheduled with companies like AQR for recruitment later in the year. Also, the program has recently roped in the Head of Research at Barclays to teach students and provide them with a connection in the industry.

Secondly, there is a lack of diversity in the incoming class, with a majority of students of Asian origin. Lack of productive group activities organized by program administrators leads to limited interaction among students from different parts of the world.

Finally, while economic understanding developed through Financial Economics courses is highly useful and necessary, it is not sufficient to excel in the quant job market. Students must consciously balance the recommended curriculum with sufficient computational and mathematical finance coursework to have a well-rounded profile. The program's flexibility and the presence of other top MS finance programs at Columbia provide ample opportunity to do so. Having said that, I think this potential confluence of coursework in financial economics and computational finance sets this program apart and uniquely prepares students to creatively use advanced mathematical concepts in an economic framework.

Below is the list of my coursework for context:

(PhD; DRO/OR) Dynamic Programming
(PhD; DRO/OR) Foundations of Optimization
(PhD; ELEN) Reinforcement Learning
(PhD; DRO/OR) Natural Language Processing

(PhD; Finance) Continuous Time Finance and Advanced Derivatives
(PhD; Finance) Finance Theory
(PhD; Finance) Microstructure Theory
(PhD; Finance) Econometrics and Statistical Inference II
(PhD; Finance) Time Series and Panel Data Econometrics
(PhD; Finance) Data Analytics in Finance

(Masters; Financial Engineering) Term Structure and Credit Models
(Masters; Financial Mathematics) Credit Analytics
(Masters; Statistics) Statistical Machine Learning

(MBA) Investing in High Yield Credit Markets
(MBA) Real Estate Analytics
(MBA) Debt Markets

Overall, the program is excellent, and students derived significant benefits from it. From my own experiences, the program met all my expectations and then some more. It provided me with a promising career trajectory, a diverse and extensive professional network, deep and meaningful personal relationships, and most importantly, a valuable and enduring learning experience.

I am currently a second-year student in the MSFE graduating May 2023. While the program is currently less known to the QuantNet community relative to the MAFN and MS Financial Engineering (MFE) programs at Columbia, this has more to do with the lack of marketing of the program historically than the success and quality of the program or our alumni and students. In my opinion, the MSFE is the preeminent quantitative finance masters degree program for students pursuing careers as quantitative researchers.

Application process: very standard, deadline typically in early January the year of planned matriculation. Had one interview with a then second-year student in the program, questions were markets oriented and asked me to explain my previous research at a high level. The then director of the program was extremely willing to talk with me throughout the process and was very welcoming.

Positives
- 10 out of the required 16 courses we take must be at the PhD level. Our curriculum in our first year is identical to that of finance PhDs, with the only difference being most of our students take a stochastic calculus & derivatives pricing course in place of the microecon sequence which PhDs take. I additionally took PhD courses from the Decision, Risk, and Operations (DRO) division of the business school and the EE department outside of the business school. Other students took PhD courses from the Statistics, Computer Science, and IEOR departments as well. While students in other quant finance masters programs ostensibly could elect to enroll in PhD coursework, we are unique in the sense that our program requires us to take PhD courses.

- Total freedom with coursework in the second year aside from the 10 PhD course requirement; ability to take masters level courses throughout the broader university, e.g. MAFN, MFE, MA Stats, etc. For many students with thorough training in mathematics (real analysis, optimization, probability, stochastic processes, etc.), the required 1st and 2nd semester curriculum in most financial engineering programs is largely review; hence, our program allows us access to these programs more advanced/niche electives while substituting their required coursework for finance PhD coursework. Additionally, we can also take MBA courses within the business school. While most MBA courses are not relevant for quant students, there are a handful which I think complement our coursework well and add an additional element which is not present in other programs.

Negatives
- Despite the small class size (the program typically enrolls 25-30 students a year), the cohort feels disjointed (students partition themselves by the region of the world where they are from). The program staff could do many things to prevent this such as having weekly interview prep sessions in the first semester broken into randomized small groups to foster interaction between all students in the program. The broader point here being there is a tangible lack of togetherness present in the program; this admittedly is an easy problem to fix in my opinion.

- Career service team (CMC) is largely completely unequipped to place students in quant finance positions and needs significant education in the field. Steve Haggerty is an advisor to the program and does an exceptional job (he leads the presentation here: https://quantnet.com/threads/overview-of-career-landscape-for-msfe-financial-economics-students.53574/). Professors are extremely willing to help students with internship/job search which helps to make up for the lacking quality of the CMC. Nonetheless, with students critically busy with coursework, especially in the first semester, the program staff needs to organize more (ideally biweekly) quant finance-specific career services events.

Final Comments
I was admitted to CMU MSCF (distinguished merit scholarship), Columbia Financial Engineering, and Chicago Financial Mathematics (70% scholarship), and feel without doubt that I made the right choice over these programs.

The MSFE is meant for students with a well-defined interest in quantitative finance and who desire rigorous coursework and the flexibility to personally tailor that coursework to their specific interests in quantitative finance.