Profile Evaluation for Top MFE Programs

[Quantgrad]

Hello everyone,

I'm seeking advice and insights on my profile's suitability for top MFE programs. I would appreciate any feedback or recommendations on how to strengthen my application or which programs might be a good fit.

Academic Background:

• Currently pursuing a Bachelor of Science in Economics at a top European university with a high GPA

Work Experience:

• Transaction Services Summer Analyst at a Big4, with a focus on financial transactions and model development.
• Finance Summer Analyst at a major sports club, involving financial analyses and budget optimization.

Leadership & Extracurricular Activities:

• Active member and leader in a university sports management and finance club.

Skills & Certifications:

• Technical: Proficient in Excel, Bloomberg, C++, Python, and RStudio.
• Certifications: Bloomberg Certification and participation in an Investment Banking Trading Academy.

Interests:

• Volunteering activities and a keen interest in sports.

Career Aspirations:

• I am exploring opportunities in both traditional finance roles and quant-heavy areas, making me consider both MFin and MFE programs. I have a strong inclination towards quantitative subjects.

Test Scores:

• Planning to take the GMAT, aiming for a 710+ score.
• SAT: 1510

I would be grateful for any feedback on my profile and suggestions for suitable MFE programs, especially considering my quantitative strengths and diverse interests in finance.

Thank you in advance!

 

[Lukee]

Whats your background in math and stats?

 

[Andy Nguyen]

Background/degree does not mention lot of math so I would assume lack of the usual prerequisite courses for the traditional MFE programs.
You can take the courses to fill the gap and apply to top programs(look at our QuantNet rankings).
If you want to keep your options open, maybe some newer programs would be a better fit. They are quant-lite and job profiles are more traditional and diverse.
Columbia MSFE (Financial Economics)
Yale Asset Management

 

[Quantgrad]

Hello everyone,

Firstly, I apologize for my delayed response. I've been deeply engaged in my exam session. I wanted to share a brief overview of the topics covered in Math and Stats modules.

Mathematics Modules:

1. Differential Calculus for Functions of N Real Variables: This included partial derivatives, first and second-order differentials.
2. Implicit Functions: Understanding and application.
3. Optimization Techniques: Both unconstrained and constrained optimization, covering classical programming and differentiable non-linear programming.
4. Dynamical Systems: Delving into ordinary differential equations, finite difference equations, including glossary and properties.
5. Solving Equations: Techniques for separable and linear autonomous equations.
6. Stability Analysis: Focus on the linear autonomous case, linearization in non-linear autonomous cases, and one-dimensional autonomous systems including phase diagrams.
7. Fundamental Mathematical Concepts: Covering Cartesian structure, linear, Euclidean, and topological structures, functions, sequences, series, limits, continuity, matrix algebra, linear functions, and operators.

Statistics Modules:

1. Exploratory Data Analysis: Basic univariate and bivariate analysis, including frequency distributions, graphical representations, summary statistics, contingency tables, and regression models.
2. Probability Foundations: Elementary set theory, events, algebras, conditional probability, and Bayes' rule.
3. Random Variables: Understanding discrete and continuous distribution, expectations, moments, and common distribution families.
4. Random Vectors: Joint, marginal, and conditional distributions, covariance, correlation, and stochastic independence.
5. Sampling Techniques: Concepts of population and sample, inferential process, and sampling error.
6. Statistical Inference: Focusing on statistical inference, sampling variability, interval estimation, confidence intervals, hypothesis testing, and regression models.

I recently took the GMAT Focus scoring 665 (94 percentile).

Thanks again for your help and support.