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C++ Programming for Financial Engineering
Highly recommended by thousands of MFE students. Covers essential C++ topics with applications to financial engineering.Python for Finance with Intro to Data Science
Gain practical understanding of Python to read, understand, and write professional Python code for your first day on the job. Coming soon.An Intuition-Based Options Primer for FE
Ideal for entry level positions interviews and graduate studies, specializing in options trading arbitrage and options valuation models.
Latest reviews
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:
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!
- GucciBandana
- 5.00 star(s)
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 -
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.
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.