How to prepare for top MFE programs application?

[Yanjia Li]

Hi, I'm a master student from Cornell and my major is information science. I'm now taking intern in Amazon but I found this job cannot inspire my passion for a long time. I'm considering apply for a MFE program to land a job in quant area after 1 or 2 years. Here's my background.

Math: My bachelor major is electrical engineering so I got foundation in math like linear algebra, calculus, probability and stochastic process. Just need some time to catch up.

Programming: I'm now a software engineer intern in an IT company and I'm familiar with lots of programming languages like Cpp, Java and Python. I'm also good at data structure and algorithm.

Finance:I never took official credit for finance and financial engineering courses. I also never taken any finance related intern or projects before.

Statistics: I've taken courses for machine learning and data mining before. I'm also good at Hadoop/MapReduce. But my knowledge about statistics is limited to big data field.

To get better prepared for applying top MFE programs/getting a quant job, I have a simple plan for myself:
1. CFA Level 1: I'm going to pass CFA level 1 to demonstrate my finance knowledge
2. Financial engineering courses: I still have one semester before graduation so I want to enroll in some financial engineering courses from Cornell FE. Are there any must-have courses?
3. Emphasis on data analysis: I think data analysis techniques like machine learning and data mining is my strength. So I guess I should keep practicing in this area?

Can you guys give me some advises on my plan or tell me how to prepare for a quant job from now on? Thank you very much!

 

[Zhenghao Li]

You don't need a MFE degree. If you're really as good as data mining as you suggested, you can just finish your CFA to boost your financial background (and trust me if you really learned the 1~2 levels of CFA carefully you would be as good as, if not batter than, a regular MFE graduate from many schools. Do some independent/paid financial research on computational finance to boost your resume and more importantly to see if you really like quant finance. And then just use your Cornell connections to find quant hedge fund/bank research recruiters. Do some research on the company before you go. Approach the people (HR, head of department, etc.), don't be shy and tell them STRAIGHT IN THE FACE:

1. I know who you're. I know what your strategy is. I know your role in the market.
(really important for hedge funds)
2. I have these skills that can help your research and I've done XYZ projects in the past that might interest you.
3. I want to work for you for the rest of my life.

It worked really well for me in the past as an undergrad. With your math background and IT programming experience it would be insane to not want to have you on board in a research department, so long as you get more finance experience.

 

[C S]

Hi, I'm a master student from Cornell and my major is information science. I'm now taking intern in Amazon but I found this job cannot inspire my passion for a long time. I'm considering apply for a MFE program to land a job in quant area after 1 or 2 years. Here's my background.

Math: My bachelor major is electrical engineering so I got foundation in math like linear algebra, calculus, probability and stochastic process. Just need some time to catch up.

Programming: I'm now a software engineer intern in an IT company and I'm familiar with lots of programming languages like Cpp, Java and Python. I'm also good at data structure and algorithm.

Finance:I never took official credit for finance and financial engineering courses. I also never taken any finance related intern or projects before.

Statistics: I've taken courses for machine learning and data mining before. I'm also good at Hadoop/MapReduce. But my knowledge about statistics is limited to big data field.

To get better prepared for applying top MFE programs/getting a quant job, I have a simple plan for myself:
1. CFA Level 1: I'm going to pass CFA level 1 to demonstrate my finance knowledge
2. Financial engineering courses: I still have one semester before graduation so I want to enroll in some financial engineering courses from Cornell FE. Are there any must-have courses?
3. Emphasis on data analysis: I think data analysis techniques like machine learning and data mining is my strength. So I guess I should keep practicing in this area?

Can you guys give me some advises on my plan or tell me how to prepare for a quant job from now on? Thank you very much!

I think you should keep on the path you are. Try to get a research oriented or at least data mining role at Amazon, Google etc. Then the quant funds will be bidding high for your services in a few years.

I noticed the other day that someone on stackOverflow with some very highly rated answers had jumped ship from Google to an HFT shop. That to me is clearly a better way to go than what you are proposing.

 

[Yanjia Li]

You don't need a MFE degree. If you're really as good as data mining as you suggested, you can just finish your CFA to boost your financial background (and trust me if you really learned the 1~2 levels of CFA carefully you would be as good as, if not batter than, a regular MFE graduate from many schools. Do some independent/paid financial research on computational finance to boost your resume and more importantly to see if you really like quant finance. And then just use your Cornell connections to find quant hedge fund/bank research recruiters. Do some research on the company before you go. Approach the people (HR, head of department, etc.), don't be shy and tell them STRAIGHT IN THE FACE:

1. I know who you're. I know what your strategy is. I know your role in the market.
(really important for hedge funds)
2. I have these skills that can help your research and I've done XYZ projects in the past that might interest you.
3. I want to work for you for the rest of my life.

It worked really well for me in the past as an undergrad. With your math background and IT programming experience it would be insane to not want to have you on board in a research department, so long as you get more finance experience.

Thank you very much. I have four financial engineering course options for next semester. Could you tell me which one is more useful for me?

Optimization
Formulation of linear programming problems and solutions by the simplex method. Related topics such as sensitivity analysis, duality, and network programming. Applications include such models as resource allocation and production planning. Introduction to interior-point methods for linear programming.

Operations Research Tools for Financial Engineering
Introduction to the applications of OR techniques, e.g., probability, statistics, and optimization, to finance and financial engineering. First reviews probability and statistics and then surveys assets returns, ARIMA time series models, portfolio selection, regression, CAPM, option pricing, GARCH models, fixed-income securities, resampling techniques, and behavioral finance. Covers the use of R for statistical calculations and optimization.

Simulation Modeling and Analysis
Introduction to Monte Carlo simulation and discrete-event simulation. Emphasizes tools and techniques needed in practice. Random variate, vector, and process generation modeling using a discrete-event simulation language, input and output analysis, modeling.

Financial Engineering with Stochastic Calculus
Introduction to continuous-time models of financial engineering and the mathematical tools required to use them, starting with the Black-Scholes model. Driven by the problem of derivative security pricing and hedging in this model, the course develops a practical knowledge of stochastic calculus from an elementary standpoint, covering topics including Brownian motion, martingales, the Ito formula, the Feynman-Kac formula, and Girsanov transformations.

 

[C S]

Thank you very much. I have four financial engineering course options for next semester. Could you tell me which one is more useful for me?

Optimization
Formulation of linear programming problems and solutions by the simplex method. Related topics such as sensitivity analysis, duality, and network programming. Applications include such models as resource allocation and production planning. Introduction to interior-point methods for linear programming.

Operations Research Tools for Financial Engineering
Introduction to the applications of OR techniques, e.g., probability, statistics, and optimization, to finance and financial engineering. First reviews probability and statistics and then surveys assets returns, ARIMA time series models, portfolio selection, regression, CAPM, option pricing, GARCH models, fixed-income securities, resampling techniques, and behavioral finance. Covers the use of R for statistical calculations and optimization.

Simulation Modeling and Analysis
Introduction to Monte Carlo simulation and discrete-event simulation. Emphasizes tools and techniques needed in practice. Random variate, vector, and process generation modeling using a discrete-event simulation language, input and output analysis, modeling.

Financial Engineering with Stochastic Calculus
Introduction to continuous-time models of financial engineering and the mathematical tools required to use them, starting with the Black-Scholes model. Driven by the problem of derivative security pricing and hedging in this model, the course develops a practical knowledge of stochastic calculus from an elementary standpoint, covering topics including Brownian motion, martingales, the Ito formula, the Feynman-Kac formula, and Girsanov transformations.

You're not going to get much from a single course, except perhaps motivation and some good background for the future. With that in mind, I'd say the one that's hardest to study on one's own would be Black--Scholes. Not because it's mathematically difficult, but because it's hard to get the raw intuition that practitioners have. It'd be useful to interact with the professor and TA(s) and get some insight. Nobody considers you a financial engineer unless you know Black--Scholes anyway (this has repercussions for the interviewing process). In reality, to make $$$ you may find the subjects in the other classes more valuable. As I suggested though, you can study and pick it up on your own as needed, and it's probably not too divorced from what you have done in machine learning.

 

[Zhenghao Li]

You're not going to get much from a single course, except perhaps motivation and some good background for the future. With that in mind, I'd say the one that's hardest to study on one's own would be Black--Scholes. Not because it's mathematically difficult, but because it's hard to get the raw intuition that practitioners have. It'd be useful to interact with the professor and TA(s) and get some insight. Nobody considers you a financial engineer unless you know Black--Scholes anyway (this has repercussions for the interviewing process). In reality, to make $$$ you may find the subjects in the other classes more valuable. As I suggested though, you can study and pick it up on your own as needed, and it's probably not too divorced from what you have done in machine learning.

OP only need to know Black-Scholes well unless she want to do derivative pricing or derivative trading, you don't need that for forex, rate, equity, bond, and etc. Financial engineers are not only those people who know about derivatives, but should be those people who really resembles an "engineer". That is, people who can create and innovate new research on various topics, from trading strategies to risk measures. It doesn't matter that you don't understand something, it's that mentality and ability to learn and innovate with new knowledge, new information, and real world data that matters the most. A financial engineer who doesn't know how to research on new ideas is like a singer who cannot write songs at all. Sometimes you forget stuff but you can always pick it up quickly. It's that mental process that most people lack.

 

[Zhenghao Li]

Thank you very much. I have four financial engineering course options for next semester. Could you tell me which one is more useful for me?

Optimization
Formulation of linear programming problems and solutions by the simplex method. Related topics such as sensitivity analysis, duality, and network programming. Applications include such models as resource allocation and production planning. Introduction to interior-point methods for linear programming.

Operations Research Tools for Financial Engineering
Introduction to the applications of OR techniques, e.g., probability, statistics, and optimization, to finance and financial engineering. First reviews probability and statistics and then surveys assets returns, ARIMA time series models, portfolio selection, regression, CAPM, option pricing, GARCH models, fixed-income securities, resampling techniques, and behavioral finance. Covers the use of R for statistical calculations and optimization.

Simulation Modeling and Analysis
Introduction to Monte Carlo simulation and discrete-event simulation. Emphasizes tools and techniques needed in practice. Random variate, vector, and process generation modeling using a discrete-event simulation language, input and output analysis, modeling.

Financial Engineering with Stochastic Calculus
Introduction to continuous-time models of financial engineering and the mathematical tools required to use them, starting with the Black-Scholes model. Driven by the problem of derivative security pricing and hedging in this model, the course develops a practical knowledge of stochastic calculus from an elementary standpoint, covering topics including Brownian motion, martingales, the Ito formula, the Feynman-Kac formula, and Girsanov transformations.

I would say Simulation/Stochastic Calculus are really important as these are considered common "skillsets" for MFE students. Other than that the Operations Research course seems nice if it involves many empirical study.

 

[IntoDarkness]

ppl with only masters normally dont get chance to "research". those jobs often reserved for phds... seems u never worked in the industry...