• C++ Programming for Financial Engineering
    Highly recommended by thousands of MFE students. Covers essential C++ topics with applications to financial engineering. Learn more Join!
    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. Learn more Join!
    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. Learn more Join!

Becoming a Quant from an Economics Background

Hi everyone

A bit of background about me:

I was pursuing a Ph.D. in Economics but decided to transition after two years because I didn't want to become a professor (also, in the interest of disclosure, the thought of an additional 2-3 years of grad student life became unappealing); therefore I am leaving with a master's degree after wrapping up my thesis this December.

My undergrad background is:
BA in Econ and Math
Key courses: econometrics, single- and multi- variate calc, linear algebra, differential equations, statistics, probability theory, complex analysis, real analysis

My grad courses include:

2 semesters micro theory: Introduced me to portfolio theory, utility theory, game theory, constrained linear and nonlinear optimization

2 semesters macro theory: Introduced me to several asset pricing models, including the capital asset pricing model; also introduced me to dynamic programming

3 semesters econometrics: covered a large variety of topics, including two stage least squares, GMM, logit and probit models, maximum likelihood estimation, serial correlation, multiple equation models, time series (ARCH, GARCH, ARMA, ARIMA), the bootstrap, some Monte Carlo

1 semester Bayesian Data Analysis

1 course on financial Economics

I took a few more courses my second year which weren't particularly relevant to the field of quant finance.

I also had an internship experience working as an economics research intern for a government agency (basically I was a glorified programmer; I did get a lot of experience with data analysis and working with databases, though).

I feel pretty comfortable working with R, Python, SQL, SAS, and MatLab. I have had a personal interest in C++ for a while so my practical ability with it is decent.

I have also done a variety of online courses through Udemy and Coursera; including one on stochastic calc, two on machine learning, and one on c++.

I have read a few books to get me up to speed with the world of quant finance, including:

1. "Paul Wilmott on Quantitative Finance" by Wilmott
2. "C++ Design Patterns and Derivatives Pricing" by Joshi
3. "Stochastic Calculus for Finance - part II" by Shreve
4. "Numerical Methods in Finance and Economics - A MATLAB-Based Introduction" by Brandimarte

So, what do you guys think? Can I apply for jobs? Do I need more formal education? Any other suggestions?

I am considering working more on the side of quantitative risk analytics rather than trading.

Thank you for reading this.
Absolutely. You just have to be really good in economics and statistics — and you should probably know a fair bit about computer science and engineering too, for roles where you have to implement your strategies.

Daniel Duffy

C++ author, trainer
Some real analysis and numerical analysis.

John Hull's book. Paul Wilmott's book is PDE-oriented. So you need to know PDE.
C++ design patterns book is outdated at this stage. The object-oriented model for patterns is not the be all end all anymore.
I like Bruce Eckel's C++ books.

C++11 - Wikipedia

The QN C++ course is probably a most efficacious way to learn C++ (disclaimer: I originated it).
Last edited:
Thank you very much for your replies, Junaith and Daniel Duffy; they are both very useful.

Daniel Duffy: Thank you for the info on C++. I have just one question for you, if you don't mind:

-I got through Wilmott's book with a solid foundation in ODEs and a shaky foundation in PDEs after learning some numerics. Wilmott's philosophy seems to be that, although PDEs are crucial, understanding the intuition behind PDEs and the numerics involved in solving them is more important than understanding them in any great analytical depth. Then again, Wilmott seems quite suspicious of pure math in general. Do you think it's worth it to put effort into learning the pure math behind PDEs?

Daniel Duffy

C++ author, trainer
You're welcome, Oborowadabinost.

All PDEs have their origins in mathematical physics and physical processes (heat transfer, fluids, electromagnetics etc.) and have been made mathematically respectable by Functional Analysis. Having solid background and insight into PDEs allow to progress to numerical PDE with confidence.
Using "PDE intuition" is fine it should be backed up by analysis, especially when doing numerics on new PDEs. Nearly all quants do numeric PDEs in a computer. Maybe 'A bit of intuition, a bit of analysis, a bit of numerics and a bit of experimentation"?

(I find the distinction between pure/applied math a wee bit artificial). I have seen that knowing both branches does no harm.

Online Courses :: Datasim

Online Courses :: Datasim
Last edited:
I’m also starting this course on November 15th. I think it is worth taking as many mfe programs ask you about your grades in these classes and the syllabus of the class you are taking. This class goes into a lot of specifics of differential equations that even most college courses do not.

Daniel Duffy

C++ author, trainer
This class goes into a lot of specifics of differential equations that even most college courses do not.

If I may say; my own background is PDE/FDM/FEM and we also take a good look at PDE in computational finance in this course. In the pat 5 years we have also supervised quite a few MSc theses on PDE+computational finance + C++.

Traditional PDE is fine but it needs to be augmented with more uptodate material IMO.
Bump. How did it go? Did you get accepted?

Was wondering the same thing....coming from a Msc econ, covered real analysis, statistics, time series and empirical asset pricing.