University of California, Los Angeles - Master of Financial Engineering

Graduating soon but I’m going to try to give an in-depth and honest review of the program for the incoming applicants. I am generally happy with my own outcome at UCLA and I emphasize this to point out my review is not an expression of personal frustration. I am appreciative of the program, so my hope is my feedback can be used by both future applicants and admin.

Student Quality:

Probably the worst part of this program. Around a quarter of the class admits to having zero prior programming knowledge and I overheard one student say they’ve never taken any calculus before. Also almost a quarter of the program has only a passing interest in quantitative finance and are immediately turned away when they realize the rigor and breadth of knowledge required, and instead pivot to targeting traditional finance roles. Few come into this program prepared with the baseline knowledge required for the coursework or about the industry.

But what’s the worst is the work ethic and culture. For the mandatory career prep coursework before the MFE started, only ~15% completed it by the deadline. The homework load is designed to be handled by a group of 4-5 randomly assigned, but every group I knew of was filled with freeloaders who did not contribute. Cheating on exams is rampant (hint to admin, nobody needs to go to the bathroom that often) and almost everyone’s assignment submissions are thoughtless ChatGPT copy/paste. Lectures are more than half empty and many TA sessions have zero attendance, yet they will complain about how hard the material is, how bad their grades are, and that the MFE didn’t help them find jobs. The department has an unofficial "no Fs" grading policy, which works in favor of many who would otherwise be flunked out.

Despite this, there are many standout individuals who are extremely intelligent and hardworking. The range of quality in this program is shocking, you have professionals with strong international experience somehow in the same cohort as fresh grads who are scared of a matrix and need AI to debug a print statement.

Sadly, behind the scenes the admin has acknowledged that most students here do not have what it takes to work in the quant industry. Admissions should tighten up standards to improve program outcomes, but it’s a chicken and egg problem. Why would good applicants want to join a program with a poor ranking, but the program will continue to have a poor ranking until good applicants join.

Courses/Instructors:

Decent range of electives with generally good quality and depth (the MBA electives are a waste of time and it’s a shame they’re offered, if you want MBA level coursework you shouldn’t be in an MFE) but I’ll focus on the mandatory courses:

Quarter 1:

Investments: The class is a little too basic, spending weeks going over how simple cash flow discounting works, but it picks up faster near the end and gets the job done. Chernov is a knowledgeable and pleasant instructor, but does not come across as very involved. Usually fair assignments and exams.

Financial Accounting: Easily the worst class in the entire curriculum. Accounting being a core class in an MFE is a questionable decision, and while Dill is a pleasant person that responds to feedback, the material is taught poorly and he is also arguably not best suited to teach this course. Individuals from traditional finance backgrounds were getting confused by in this accounting class. This class should be compressed into a 2-week crash course before program starts and replaced with another; dedicating a quarter of the most important semester in the internship search to accounting is a terrible decision. Fair assignments and exams, despite having to pay to do weekly assignments on an online textbook.

Stochastic Calculus: On the opposite side of Accounting, this class is amazing. Panageas is a passionate instructor and this material is challenging but the class is designed for you to pass. His explanations and derivations in class are phenomenal at presenting complicated concepts in simple terms. Challenging assignments but very fair exams.

Econometrics: Another good stand out in the first quarter. Despite Yavorsky coming from a marketing background, he is exceptionally knowledgeable and skilled at presenting econometrics concepts at a technical level. He will not rest until you truly have learned the material, and you will learn a lot of material. Only downside to this class is it is taught in R (including a 1-week R bootcamp before class), let’s let that language die already. Relatively easier assignments but harder yet still fair exams.

Quarter 2:

Derivative Markets: The highlight (or lowlight depending on what kind of student you are) of the entire MFE program. This class is no longer the cake walk described in earlier reviews, it is extremely rigorous and technical and builds on Stochastic Calculus. Reiner is a passionate instructor who knows almost everything there is to know about derivatives, although sometimes he has trouble communicating it to us who are seeing most of this for the first time. It also feels like we are trying to learn too much in too little time or not spending our time on some material wisely. The TA sessions for this class are also a standout in terms of expertise and difficulty, one of the very few TA sessions attended by most students. The assignments are usually very hard and lengthy, as well as the exams. Students perform so poorly, a 0 on his midterm got you a B+ after the curve this year.

Empirical Methods: An extension of Econometrics which is taught by Lochstoer who again is very knowledgeable and pleasant individual. Useful material, but the class doesn’t stand out either positively or negatively. Maybe a little bit too much time spent on old topics like Fama-Macbeth but otherwise a good class. The assignments and exams are of medium difficulty.

Fixed Income Markets: Picks up where Investments finishes off. While it starts again quite slow, it goes into great technical depth of fixed income pricing. Longstaff is an expert, and his strength comes in explaining these hard concepts in layman’s terms during the lecture, then having us go on to implement these ideas in more technical ways during assignments. His assignments vary from easy to hard (but always fair) and his exams were on the harder side.

Trading, Market Frictions, and FinTech: This is an MBA level course that doesn’t go beyond the surface level of a variety of topics and was considered a running joke amongst the entire cohort. Zhang is again friendly and approachable, but her demeanor doesn’t compensate for bad material. I understand the intention behind a class like this, but it needs to be revamped and increased in depth to bring it to the level of quantitative finance. Extremely easy assignments and exams, just forget this class exists.

Quarter 3:

Risk Management: A useful course taught by Haddad. Haddad has an important quality of being able to explain why this material matters and will schedule extra time out of his day to help the class succeed. The main criticism is that it feels like the whole class is just spent talking about different variations of VaR which gets quite repetitive. Medium difficulty assignments and exams.

Data Analytics & ML: A continuation of Empirical Methods taught again by Lochstoer. More interesting but still relatively foundational topics discussed such as non-linear models, otherwise runs exactly like Empirical Methods. This was recently made a mandatory course, which I believe is a positive step for the program. Same difficulty as Empirical Methods.

Quarter 4:

Applied Finance Project (AFP): AFP can be hit or miss depending on the firm you are anonymously selected by and the work group you have formed. This isn’t a class in the traditional sense.

Career Services:
Antoine runs the career team with Jeremy assisting on the administrative front. Both are extremely approachable for help or advice, and Antoine’s strengths lie in creating and rebuilding the alumni networks (alongside Leanna in the admin). There is an annual NY career trip that has been moved to October, to be more in line with recruiting timelines. They also have a resume book and occasionally invite industry speakers, but the career impact of these efforts is not very strong.

Unfortunately, that’s where the positives of the career team end. Nobody has any first-hand experience with quantitative finance, and at times their efforts may even be detrimental. This cohort was forced to attend 9 hours of mandatory MBA coaching on “how to build a story in your interview”, something that would probably get you laughed out of a quant interview if you couldn’t pass the technicals. They do organize technical sessions (QIPS) but these are infrequent, loosely structured, and don’t fulfill the technical preparation needs. Targeted technical preparation is the key to passing quant interviews, and there isn’t anywhere near enough emphasis put on it.

The people in the career team are amazing individuals, but they need a hands-on expert who knows what they’re doing in this industry. Don’t rely on the them to be anything other than a supportive shoulder to cry on. Career outcomes are weak and have been getting worse over time, most graduates don’t go on to work in quantitative finance at all.

Overall:

UCLA’s MFE ranking is deflated by the poor admit quality and career outcomes, but the program instruction is still relatively competitive (top 10 QN peer score, actually above MIT and NYU Tandon). Admin is composed of great people, acknowledges most of its weaknesses, and are attempting to make some changes, but it’s not enough. It also seems like they’re hampered a lot by the department, maybe this is the business school (dis)advantage they always advertise. At the end of the day, this is just another cash cow program targeting internationals just like almost every other MFE out there. Potential applicants, keep in mind what’s hidden in these rankings is there is a selection bias; having met individuals in all MFE programs, it’s not as if Baruch or Berkeley will transform you into expert quants after a year of coursework, they simply tend to admit only those that have the highest probability to succeed in the industry in the first place.

If you are an international applicant, are passionate about breaking into the US quant finance industry, are willing to put in a lot of work inside and outside the classroom, and are able to pay one of the highest tuition fees (and worst scholarships) amongst MFE programs, UCLA’s MFE will get your foot through the door. If you do not tick all these boxes, do not go here.

This last piece of advice applies not just to UCLA MFE applicants but all MFE applicants, perhaps consider a technical non-MFE graduate degree instead (CS, Stats, OR, etc.); something you won’t be told a lot is that all MFE programs are blacklisted at many top quant firms.

One thing that really frustrates me about UCLA MFE Class of 2024 is the obsession with GPA. People spend so much time grinding through course materials—even when they’re not relevant to job interviews or real-world skills. The second quarter is already intense, with everything graded on a curve, but the focus feels completely misplaced.

If you’re serious about landing a full-time job in the U.S., GPA is not the key. What really matters is U.S. internship experience, technical skills, and networking(feedback and referral). I’ve spoken with many industry professionals—some UCLA alums, some complete strangers—and the advice is always consistent: invest your time in gaining experience and preparing for interviews. No one cares about your GPA as long as you don’t fail. You don't need to include your GPA on resume.

Don’t count on career services to carry you. You have to take initiative—network actively, get feedback from people in the industry, and do mock interviews with peers (which is surprisingly hard to arrange because everyone’s busy studying) and experienced mentors.

Honestly, this GPA obsession is exactly why our class has such a low placement rate. People are optimizing for the wrong metric.

Although we had great professors and excellent classmates, there was no 6-month AFP (Graduate Project) and no help from summer interns. We only had one week of AFP, which was the week before graduation. Employment is extremely bad as you might see from the official website. As long as you are accepted by the school from the east coast, do not consider UCLA MFE.

Just graduated last December, working in asset management now. UCLA MFE is definitely a good program. The courses covers everything you should know for quantitative finance. Definitely comprehensive knowledge and top-class professors! The only downside might be the homework, which make my life during job search a bit more stressful (because need to take care of both side). Career service is nice, I always learn sth from my conversation with Amber. Resume book sending out to employers is very helpful, I actually got my internship through resume book. Alums are helpful and nice, most of them are willing to connect and share their experiences! Our opportunities is not limited to west coast, there are a bunch of people from my class working in NY or Chicago as well.

As a nerd focusing on techniques and taking courses, UCLA's MFE brings me a different experience, reshaping my mind. Students in this program usually come from very different backgrounds. And we are required to finish almost all the coursework as a study group. In the process, I realized my advantages and learned how to work with people from different backgrounds. Such practice is helpful since it simulates the real working environment in financial institutions.

The career office provides guidelines for job searches and many networking events. They hold workshops regularly to give suggestions based on students' personal experiences in job searching. They also build a brand in the financial industry, which enables me to find my internship and current job. Although they don't appear in the referral directly, I can feel the connection during the interviews.

To be honest, without much finance background, it's hard for me to maintain GPA in a fast-paced program, search for internships/full-time jobs, and participate in many networking events at the same time. In the short 15 months, I made many mistakes and wanted to give up switching to the financial industry. However, gradually, I am more confident to face the difficulty. I owned it to my friends in the class and the good suggestions from them, alumni, and the career office. Compared with before, I already became a person with the confidence to solve all kinds of problems at work. In the end, I want to say, even though the whole process is somewhat suffering, you can gain what you want.

Some background: I just graduated in December, I had no finance background before the MFE program, and I’m a social person.

UCLA’s MFE program was my first choice when I was filling out applications and I’m convinced it’s still the best spot for quantitative people who also understand that having fun outside of class is important. My classmates were so much fun and are super smart. We had parties every week and became great friends. I doubt you’ll see the same level camaraderie at Berkeley, and I’ll bet anything you won’t see it at Baruch.

The program takes you from 0 to 100, giving you everything you need to enter the finance industry with a solid job. I just signed my full time offer letter and will be making 3x what I was making 18 months ago. The MFE staff/team is very responsive and they put in a lot of effort. Most of them have been there only a couple of years and have made great changes to the program (e.g. moving away from R to exclusively Python, getting more interaction with other programs in the business school, etc.).

You’re not going to leave this program an expert in any one field, nor should you expect to. Instead, you will leave with foundational knowledge in many fields that quants are typically interested in (ABS, equities, options, risk mgmt, ABS, data science, etc.). If you can handle a tough workload, learn fast, and work well with others, it doesn’t get much better than this. And when you’re done with class, you walk onto one of the most beautiful college campuses in the world with a bright, sunny day.

Good luck!

Class of 2023.

It seems like a money grab by the business school. Tuition too high for what we are getting.

Professors are just here to to the minimum work.

Forget about any support if you go to their cabin with any doubts after class.

Some are so stupid that don't even record the session that is on zoom. Eg QIPS. Cheating on exams is rampant.

Admin is so happy with the substandard ranking and will boast about it everywhere.

Attending the UCLA MFE program was one of the best decisions I’ve made.

In terms of academics, you'll be learning from highly regarded professors: Rossi, Longstaff, Chernov, Lochstoer (just to name a few), who are top-notch in industry and research.

In terms of structure: the 10-week quarters go by very quickly. Classes start with the basics, but pick up fast. I recommend getting good at R as you'll be doing a lot of your HW with it.

Career services: you'll receive regular emails for internships/jobs and you'll meet with the MFE career services staff to go over interview topics/questions you may have. Leanna, Olga, and Amber are a delight to work with!

Overall, it’s a great program. The coursework can get heavy at times. But the professors and staff are invested in their students’ success and want to see them succeed. And the weather in LA isn’t too bad either!

A great program, especially for someone without a rich quantitative background. I had a traditional finance background (wealth management) and gained many quantitative skills throughout the program (although it was a challenging program given my lack of quantitative skills). Had a challenging time finding a summer internship due to my lack of quantitative experience. However, I was able to land a quantitative job upon graduation. Most alumni I reached out to through LinkedIn were very friendly and helpful and some of them even offered to refer me to their company.

2014 Graduate.
Easily the most valuable year+ of my life.
Professors and coursework align on learning skills that can be applied directly to the industry. Support staff is incredible, with a strong focus on ensuring you will have multiple job offers by the end of the program. Peers in the program are talented and driven. I still stay in close contact with several people from my cohort. Highly recommended!

A great program that teaches you theoretical knowledge and gives you applied opportunities. Amazing industry partners for the AFP project. A truly dedicated career office that is focused on providing tech skills, interviews, and resume workshops.

Curriculum:
This program would teach you all the fundamentals you need to succeed in the quant finance field with world-class faculties. I personally liked the courses for their nice balance between finance, coding, and mathematics. The first two quarters build up your fundamentals in finance and more, and the third quarter takes you deeper into different fields such as asset management, data science, pricing and valuation, risk and management, etc, where you may discover your skills and passion. The courses mostly use Python, with some using R, but there is no class that specifically teaches you how to code, which is something I strongly suggest adding. You will have to figure it out yourself and go deeper if you want to ace those coding interviews. There is also an applied finance project that gives you a sneak peek of what problems the industries are trying to solve. Depending on the company, you might be solving the most difficult or weirdest problems, but it would be fruitful and fun if you step up and see the project through.

Career:
The career service team is great and proactive. They help with behavioral/ technical interviews, resume/ CL, webinars to help you understand the industry, salary negotiation, etc. They also have connections to many companies that sometimes directly hire from the program (I got my internship this way). They also still keep in touch with me even though I already graduated.

Overall I really recommend this program. It helped me transition from a pure finance background to landing a quant research role. The program gives you everything you need and more, the rest is up to you. I also want to add that most students here are pretty laid back and you will probably meet the smartest and most accomplished students here.

Awesome experience.

Education:
Excellent faculties with great knowledge. Teaching is mainly application based, So it is good for people with little less math background.
Concepts are explained in both ways mathematical and Economical.

Career:
Career service is excellent. Strong Alumni network. You can choose from many different career paths.

Great place. The classes are very practical and applicable-for me, I found the Derivatives Market and Computational Finance class most useful; the only caveat is the Quant Asset Management, as the professor does not teach the class in an easy-to-understand way. In terms of workloads, the Spring Quarter is going to be the heaviest, but that’s how you learn the practical staff. There are also a lot of helpful job searching resources-for instance, the workshops on quant interview and R/Python. In addition, the alumni network is also valuable-I find both my internship and full-time job thanks to its help!

All Faculties are world-class, Anderson is also prestigious and owns great resources and industry connections. The course is well-structured and provides practical projects align with the materials. Also, the program are heavily focus on students, any voice from student will be seriously considered and provide supports. You can see the program is trying to be better in every aspect. All in all, it's a great program and provides competitive edges for students to further their careers.

I am an alumni from the 2017 batch.

Faculty:
As it has been mentioned before, the faculty is top notch. Profs Longstaff, Ivo Welch, Lars Lochstoer, Peter Rossi are world class professors.

Structure:
It's a very packed degree, with a lot of things to digest in very little time + a summer internship. In my opinion, it's easier to absorb all the knowledge with previous experience either in finance, math or coding. Probably 2 out of the 3 would be enough to have a strong experience in the program.

Support system:
There is a good infrastructure around you. Industry professionals are invited to speak every week or so, you get mock interview sessions, resume preparation sessions, quantitative interview preparation sessions, networking events and so on. I've stayed in touch with the MFE Office and always received all the support I've needed.

For some of the students (me included), learning to connect and network takes some time, and it's hard to do this while staying afloat in your coursework. It's a very demanding 15 months but it's an experience I would definitely recommend!

Faculty:

The professors and faculty are world class. From Peter Rossi, to Francis Longstaff, to Ivo Welch, I've never had the privilege of learning from a more accomplished and well-respected group of people. The level of care they put into each of their lectures was abundantly clear and was leagues above what I experienced at my undergraduate university. They were easy to chat with, always accommodated time after class for additional questions, and, in my opinion, the main reason someone should attend UCLA's MFE program.


Coursework:

The coursework was expansive and quite rigorous. However, there is simply too much information to try and squeeze into a 15 month program. As a result of this squeeze, you feel like you're trying to drink from a fire hose. The flip side of this is that you get excellent exposure to a bunch of different subject areas in quantitative finance and, if you didn't truly have enough time to learn it the first time, you have the tools/materials/means to review it later.


Career Services:

A lot of the posts here are being hard on the MFE's career services, in my opinion. While it took me a nerve-wracking amount of time to obtain my summer internship (got my offer in late March), this wasn't because of a lack of opportunity. I must have had six or seven opportunities before winter break and I simply didn't perform in my interviews. While these were very competitive positions (Morgan Stanley, JP Morgan, etc), it's not the career services' fault that I fell short. I suppose I could say that I wasn't properly prepared to interview but that falls more on myself than the MFE program. A counselor can only take you so far before you have to take responsibility for your own career.


MFE Office / Administration:

I can't thank the lovely ladies in the MFE office enough. Leanna and Elisa were always there to listen to my grievances, help me with petty (and major) problems, and guide me through the program. The amount of students, current and prospective, they have to manage is astounding and they each deserve a raise for all the work they put in.


- Class of 2019 -

Having completed an year in the program, I believe that I am now in a position to give an objective feedback. I will address each of the quarters in the program separately.

My background:
Undergraduate form one of the top engineering institutions in India, and with cumulative work experiences of three years in a BB and asset management.

Quarter 1: This quarter has "four building blocks" of our program: Financial Accounting, Econometrics, Stochastic Calculus, Investments.
Financial Accounting: This focuses on the fundamentals of reading (just reading) the financial statements of companies. The course did not, at any moment ,focus on the relevance of each of the concepts in valuation. I finished this course with the hope that the "Financial Decision making"(Corporate finance named fancily) would cover these. More on this later.

Investments: This course is taught by Prof. Chernov and the concepts covered key concepts involving valuation of financial market products, and the theory of asset allocation. The subject was taught well and the assignments were well designed to facilitate learning.

Stochastic Calculus: The course is taught by Prof Panageas, who teaches so well that a person with no background in advanced calculus or probability can get it. The assignments in this course were well designed to reinforce concepts dealt with in class. This course is however introductory only.

Econometrics: Professor Rossi is an excellent teacher and is an expert in this subject. However, I personally felt that this curriculum was similar to that of a STAT 101 course and more content can be included in the curriculum. Devoting 6 lectures (18 hours) to linear regression is definitely overkill.

Quarter 2:

Empirical Methods in Finance: This course builds upon our fundamental Econometrics course, and a large chunk of it was modelling of time series. This course was a good refresher for me, and professor Lars did an excellent job teaching it.

Derivatives: The course is extremely basic and is not a MFE level course at all. The curriculum is best suited for MBAs, and not suitable for someone who looks to trade/price these securities. Out of the 10 lectures, NONE of them was new to me, or to anyone who has attended the first quarter.

Fixed Income markets: This course is taught by professor Longstaff and is the best course of the lot. The course builds a good understanding the fixed income products and their pricing. Professor Longstaff has tremendous experience and does an excellent job in giving the right intuition. The homeworks are realistic and extremely well structured.

Corporate finance: This course was taught with a lot of animation and the classes were indeed a good break. The course however, like accounting, was poorly structured to meet the needs of an MFE.

Quarter 3:

Financial Risk Management: This course was taught very well by Prof. Haddad and the homework assignments were interesting and exciting too.

Quantitative Asset Management: This is the worst course in the curriculum and a complete misnomer. The course, like derivatives just repeats content and the homework assignments were even more pointless (at least that wasn't the case in derivatives). The outcome of this course is just frustration and not a solid understanding of portfolio management.

Data Analytics/Machine learning: Taught by professor Lars. Although the course is taught well, and there is zero redundancy, it speaks little on application of machine learning techniques to solve real problems and is just unfortunately just involves using basic R packages to "small data".

Computational Methods in Finance: Focuses on implementation of Monte carlo and other numerical techniques to derivative pricing. It is just a repeat, that develops little understanding of the techniques. The homework assignments were redundant and just served to induce boredom.

Other important aspects:

Co-students: A large chunk of students in the class have absolutely no quantiative background, to the extent that they haven't even heard of "Matrices", get intimidated by seeing the "integral" symbol, do not understand conditional probability even after completing two quarters, and have poor programming skills. Most of them are straight out of undergrad. Although the program offers a paltry introductory math course, I believe this course doesn't serve to improve their understanding.

The review below that strongly advocates for using "Accounting" and "Fama Macbeth regressions" to build trading strategies only serves to demonstrate that people are just unaware of the difference between: expectation and average & attribution and trading. I admire your confidence in betting and LOLing people.

Homeworks: While the majority of assignments are good, some are simply redundant and repetitive. In my opinion, these homeworks should be scrapped. Also, there must be a strong restriction placed on using libraries in homeworks (which defeats the purpose).

Exams: They are just too easy and I actually experience that my undergraduate exams were way harder. The program should acknowledge that correct grading, and a necessary spread in scores are important to enhance the credibility of your grades.

Career services: You are pretty much on your own.

I’m proud to be the UCLA Anderson MFE alumni.
I would not trade my MFE experience @ UCLA for anything else, and let me tell you why:
(1)well structured classes. From basics to extremely complex concepts - everything will be covered. You’ll have plenty of opportunities to test your knowledge working on various problem-sets. It’s laughable to read complaints about Fama-MacBeth and Accounting below. I’d bet those folks just never built a single trading strategy.
(2)team work. Significant part of home assignments are done in small groups. This allows exchange thoughts with peers, improve understanding of material (incl. Fama-MacBeth;) ), and strengthen soft skills that most quants are missing.
(3)ability to provide constructive feedback and be heard. If something is not right, just let administration know. They are there for you to help and make things right. Never hesitate to schedule a meeting with the program’s Executive Director or Career Advisor to discuss suggestions, go through your resume, practice answering interview questions or get advice on job/internship search process.

I’m thankful for help and endless support of members of faculty and administration.
It’s a true honor for me to be part of strong and successful UCLA Anderson MFE family!

Irina (Class of 2017)

I am a UCLA Anderson Alumni from the MFE program class of 2016. Overall the program was stellar. Excellent professors and great guidance for our careers. We had industry professionals come give talks on a weekly basis. The staff was great as well. Elisa Dunn — the Executive Director of the MFE Program — was extremely helpful in assisting me to learn about various industries, develop my career and get a feel for the quant industry as a whole. Leanna Cortez — the Associate Director of the MFE Program — was also excellent in guiding the program and coursework. All the staff were professional and there to help. The professors were phenomenal. A lot of them have invented the knowledge they were teaching and were super clear and concise. In terms of landing a job, some people think that the career services should hand them a job. But that's not how it works. They were there to give the students opportunities and it was always up to the student to take advantage of those opportunities. I do agree with other posts with respect to Python over R and I think this is one of the parts in the MFE program that they definitely need to change. When I graduated companies only wanted Python and not R. It really helped to understand the theory and having gained the ability to program helped too. There is no way I could have gotten to where I am today without having this experience on my resume and learning the skills that were taught during the program. Highly recommend this program to advance your career and get into the company of your dreams.
Jason C. Mercurio