There is a long story to tell. Certainly, I could not finish today. I will try to write as much as I can later on. Fordham, to be honest, is the transitional point in my life, even viewed ten or twenty years later.
I came from a top 10 university in China, with its reputation being one of the earliest universities in China that opened undergraduate programs of Financial Engineering. Viewed from my master training, to be honest, teachers at my undergraduate school do not have fully technique knowledge of continuous-time finance, though they do have the intuitive understanding of all concepts and try to teach us as much as they can.
Entering Fordham MSQF, I felt extremely hard at the very beginning due to lack of training in programming. Fortunately, I had audited four key graduate courses in Financial Engineering in my last semester at my undergraduate school, I did have more knowledge of Econometrics, derivatives, and a little bit continuous-time finance (just two-weeks in Intro to Stochastic Calculus). Therefore, I spent much of my time learning coding details. However, the course schedule is too intensive that I could not learn VBA quite well in the first eight-week course called Advanced Financial Modelling taught by Prof. Blackburn. I finished half coding in the midterm exam in the fourth week and a little bit more than half coding in the final exam in the eighth week. Honestly speaking, Fordham MSQF program is more intensive than many of Econ/Fin Ph.D. programs at universities ranking around 80-120, in terms of homework and the difficulty of materials.
To be continued...
C++ Programing for Financial Engineering Online Certificate. Approved for 15 CPE credit hours by GARP
About me: graduated recently from 2016 batch. will start my job in Jan in complex securities valuation.
My colleagues have already given a comprehensive outline of the program. I am going to add what I think makes UCLA MFE Program special and what needs to be improved.
What’s special about the program:
1- The admission process is one of the best: smooth, professional and friendly. Depending on your background, there will be one or two rounds of interview.
2- It’s a business school! Having access to such a rich network is extremely invaluable and goes a long way for job placement. (As a rule of thumb, programs offered by business schools usually require GMAT rather than GRE) Networking is an essential part of job placement.
3- It’s Los Angeles! If there is a heaven on earth, LA is it! Great Weather, Friendly people, lots of space, delicious foods, diverse culture. these will smooth your transition into a new country and improve your quality of life while studying.
4- Considering the variety of jobs looking for MFEs and extreme competition to get those, it is really beneficial to know what type of job you are looking for upon entering such program and to create your own brand from the very first day. UCLA MFE prepares students best for quantitative asset management, valuation and advisory, data analytics, risk management and statistical arbitrage roles (which mostly have python/R/Matlab as core programming language.) If you are specifically looking for jobs requiring C++ such as High-Frequency Trading, then it may not be a good option.
5- Faculty is definitely an advantage. Even mentioning that you have worked on your applied finance project with well-known names in academic and industry such as Professor Longstaff makes you stand out among other applicants in an interview. Professors such as Daniel Andrei, Levon Goukasian are so dedicated that you can reach them anytime with your questions (even after graduation).
6- It’s 13 months! (it can be an advantage or disadvantage!!) a trade-off between higher program intensity and lower cost of living and opportunity cost.
7- This program helps you to build on top of what you already have: if you do not know anything about derivatives, it will teach you the essentials. If you know the basics, you can get an in-depth knowledge of derivatives. And if you are already an advanced user of derivatives, you will have a chance to work on various models, understand their dynamics and hone your coding skills. So COME AS PREPARED AS YOU CAN!
What can be improved:
1- Career services: Considering the number of students, career services department is understaffed. The program deserves an image and footage at least as good as the program itself. Career services team needs both HR and technical members to introduce the program and its students’ qualification. A well-structured program without a proper marketing and presentation.
2- The program should have started earlier in October (which is starting now with the current batch) to equip candidates with requisite math, statistics, programming and finance essentials in the first quarter. We needed more time to digest all the materials in the econometrics course in our first quarter.
3- More flexibility on electives.
Going back, I would definitely choose UCLA MFE in my top three. If I could bet on school future rankings, I would go long this program as I believe it to be greatly undervalued in quantnet ranking.
Graduate from 2016 batch with work experience in Banking and Risk Management. Currently working in Financial Services/Accounting.
Personally, I found the UCLA MFE experience satisfying and enriching. Having considerable experience in the field of banking, I was expecting to gain skill-sets for asset management and other industries such as consulting, Fin-tech. MFE courses and projects gave me the flavor of most of the career paths available for a financial engineer.
Some Plus points:
UCLA MFE faculty is world class. It was amazing and very exciting to learn from professors such as Prof. Rossi, Prof. Schwartz, Prof. Longstaff, Prof. Goukasian and others. Classes such as Fixed Income, Computational Finance, statistical arbitrage taught me practical application and issues with theoretical finance concepts.
The program schedule is very hectic and definitely not for the weak-hearted. The program is supplemented with weekly finance seminars and group activities that helped us widen our knowledge base and perspective. Courses such as Computational Finance, Fixed Income, Quant Asset management provided a lot of hands-on experience for financial model building, model validation/calibration, valuation, and backtesting, which was immensely helpful in our internships. The majority of courses required coding in R/Matlab or Python.
UCLA MFE has the dedicated career service. I felt that career service is understaffed considering the total number of students and the vast variety of career paths. Career services regularly provided us with openings/opportunities with frequent workshops on skill enhancements and guest lectures from successful finance professionals. This year most of the openings were in Risk, valuation and Fin-Tech. There were opportunities for networking with LA-based firms in the form of guest lectures/seminars/events. But it definitely takes effort from student side as well to get a job/internship. In my batch, Students with good tech skills (programming) found it easy to secure an internships/jobs in an early phase. Most of the top banking internships get finalized by Jan/early Feb, so it might be hard to target these internships as most of the relevant courses are taught in 2nd and 3rd quarter.
There is no place like LA. More I traveled to other places, more I loved LA. Amazing weather, amazing neighborhood (Beverly/Hollywood) and a great campus.
Some things which can be improved:
Career services team is bit understaffed and sometimes it is too much work to handle full-time jobs and internships of 2 batches simultaneously.
Less exposure to Python which is now used almost in every quant role.
There is less flexibility with electives and program structure.
UCLA MFE is definitely an amazing program and greatly undervalued in Quantnet rankings. The program is very receptive to feedback and some of the changes that we suggested are already being incorporated. I am pretty sure that with a growing alumni network and proactive changes, UCLA MFE will be one of the top programs in next few years.