- Joined
- 9/25/24
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This will contain quite a bit of information about myself and I hope to gain an insight into the profile of a Quant Masters candidate in both the UK & US.
I'm currently on my third year of study (out of 4 years) at a fairly known school in the U.K, it's not a top school, and I'm on a study year abroad at one of the most prestigious schools in Australia. I'm currently a BSc Economics student and have taken these relevant modules:
Math:
- Intro to Math for Finance & Economics (Calculus 1, Partial Derivatives)
- Math 1B (Calc 2 / Intro to Calc 3, ODE, Linear Algebra, Probability & Statistics) | Not Graded Yet (Study Abroad)
- Higher Several Variable Calculus | Yet to do (Study Abroad)
- Applied Mathematics for Economists (Intro to Linear Algebra with applied optimisation techniques) | 70% (Top percentile in cohort)
- Higher Linear Algebra | Yet to do (Study Abroad)
- Higher Theory & Application of Differential Equations | Yet to do (Study Abroad)
- Mathematical Economics (Basic Analysis, Topology, Static & Dynamic Optimisation Techniques) | Yet to do
- Numerical Methods & Statistics | Yet to do
Statistics / Econometrics / Probability:
- Introduction to Econometrics | 69%
- Game Theory and Business Strategy (Set Theory, Probability, Market Making Games, etc.) | Not Graded Yet
- Applied Econometric Methods | Not Graded Yet
- Applied Econometrics | Yet to enrol
- Applied Statistics for Finance (Maximum Likelihood Estimations, Parametric / Non-Parametric Testing, Time Series Analysis, Stochastic Processes, CAPM, ANOVA) | 67%
- Mathematical Statistics (sampling distributions, inference, joint distributions, maximum likelihood estimation... etc | Yet to enrol
Programming:
- Data Coding & Visualisation (Python) | 74%
- Economics & Data Science | Yet to enrol
- Coursera Data Science Course in Python & SQL | Currently Doing
Those are all relevant modules, I also may try to self-study real analysis and some more probability or if not take a summer module for it before applying to masters programmes.
I am on track to graduate with a 4.0 GPA hopefully, I'm currently on a 3.9 overall for my first and second year.
Work Experience / Internship:
Junior Research Associate - Individual Research with Supervisor (Funded).
My research involved in an depth analysis of linear algebra and convex optimisation algorithms with implementation in portfolio optimisation, holding a more mathematical and theoretical approach as I like to explore the reasoning behind methods used in applied mathematical fields such as finance / economics.
I optimised portfolios within Python and wrote a dissertation on a comparative analysis between linear and quadratic methods by utilising large databases like yahoo finance and Monte Carlo simulations for applications of my algorithms. In particular I focused on the Markowitz portfolio optimisation model for the quadratic optimisation process and compared it to a direct linearised version of it for the linear programme, the study itself delves into the nuance differences in the models and the reasons why.
From this I was able to rigorously test the models for computational efficiency, accuracy and so on to draw up comparisons and evaluate the benefits of the individual and a collective, that being perhaps a bank or a hedge fund, that would utilise either/or optimisation method in a portfolio optimisation model.
Given these modules taken / to be taken and experience, how can I determine my fit for US/UK quant programmes based on my academic profile, school and major.
My schools of highest interest are:
UK:
Imperial, UCL, LSE, Oxford.
US:
Berkeley, CMU, Uchicago, Baruch, MIT, Upenn.
I am more inclined to stay in the UK for my studies just from a cost perspective but I also much prefer to study in the US for opportunity in a bigger Tech & Quant market that has much better payoffs and opportunity from what I've seen. If I could receive some advice to enhance my profile and decisions that would help, thanks.
I'm currently on my third year of study (out of 4 years) at a fairly known school in the U.K, it's not a top school, and I'm on a study year abroad at one of the most prestigious schools in Australia. I'm currently a BSc Economics student and have taken these relevant modules:
Math:
- Intro to Math for Finance & Economics (Calculus 1, Partial Derivatives)
- Math 1B (Calc 2 / Intro to Calc 3, ODE, Linear Algebra, Probability & Statistics) | Not Graded Yet (Study Abroad)
- Higher Several Variable Calculus | Yet to do (Study Abroad)
- Applied Mathematics for Economists (Intro to Linear Algebra with applied optimisation techniques) | 70% (Top percentile in cohort)
- Higher Linear Algebra | Yet to do (Study Abroad)
- Higher Theory & Application of Differential Equations | Yet to do (Study Abroad)
- Mathematical Economics (Basic Analysis, Topology, Static & Dynamic Optimisation Techniques) | Yet to do
- Numerical Methods & Statistics | Yet to do
Statistics / Econometrics / Probability:
- Introduction to Econometrics | 69%
- Game Theory and Business Strategy (Set Theory, Probability, Market Making Games, etc.) | Not Graded Yet
- Applied Econometric Methods | Not Graded Yet
- Applied Econometrics | Yet to enrol
- Applied Statistics for Finance (Maximum Likelihood Estimations, Parametric / Non-Parametric Testing, Time Series Analysis, Stochastic Processes, CAPM, ANOVA) | 67%
- Mathematical Statistics (sampling distributions, inference, joint distributions, maximum likelihood estimation... etc | Yet to enrol
Programming:
- Data Coding & Visualisation (Python) | 74%
- Economics & Data Science | Yet to enrol
- Coursera Data Science Course in Python & SQL | Currently Doing
Those are all relevant modules, I also may try to self-study real analysis and some more probability or if not take a summer module for it before applying to masters programmes.
I am on track to graduate with a 4.0 GPA hopefully, I'm currently on a 3.9 overall for my first and second year.
Work Experience / Internship:
Junior Research Associate - Individual Research with Supervisor (Funded).
My research involved in an depth analysis of linear algebra and convex optimisation algorithms with implementation in portfolio optimisation, holding a more mathematical and theoretical approach as I like to explore the reasoning behind methods used in applied mathematical fields such as finance / economics.
I optimised portfolios within Python and wrote a dissertation on a comparative analysis between linear and quadratic methods by utilising large databases like yahoo finance and Monte Carlo simulations for applications of my algorithms. In particular I focused on the Markowitz portfolio optimisation model for the quadratic optimisation process and compared it to a direct linearised version of it for the linear programme, the study itself delves into the nuance differences in the models and the reasons why.
From this I was able to rigorously test the models for computational efficiency, accuracy and so on to draw up comparisons and evaluate the benefits of the individual and a collective, that being perhaps a bank or a hedge fund, that would utilise either/or optimisation method in a portfolio optimisation model.
Given these modules taken / to be taken and experience, how can I determine my fit for US/UK quant programmes based on my academic profile, school and major.
My schools of highest interest are:
UK:
Imperial, UCL, LSE, Oxford.
US:
Berkeley, CMU, Uchicago, Baruch, MIT, Upenn.
I am more inclined to stay in the UK for my studies just from a cost perspective but I also much prefer to study in the US for opportunity in a bigger Tech & Quant market that has much better payoffs and opportunity from what I've seen. If I could receive some advice to enhance my profile and decisions that would help, thanks.
