Ph.D. Prep – Got Math?

Both math and economics are important in preparing for doctoral studies in Finance.  Statistics training is also a very good alternative to economics.  One of the first professors I talked to about getting a Ph.D. post-MBA gave me a list of courses to have under my belt.  I wasn’t able to take all of them, but I ascribe a significant portion of my success in getting into school to following his advice (and doing very well in the courses).

Students planning on getting a doctorate degree in Finance or Economics should choose one or two math-heavy majors.  Math, Economics, and Statistics are probably the best, but there are others that could also work.  John Cochrane, Professor of Finance at the University of Chicago, studied Physics before doing his Ph.D. in Economics at U.C. Berkeley.    The most useful courses include:


  • Math – Full Calculus sequence (three-semester sequence for me)
  • Math – Linear/Matrix Algebra
  • Econ – undergraduate-level Price Theory (two-semester sequence for me)
  • Econ – undergraduate-level Financial Economics
  • Econ – graduate-level Price Theory
  • Econ – Statistics for Economists (there may be a comparable stats course)
  • Econ – undergraduate-level Econometrics (my school offered two of these, and I took them both)
  • Econ – graduate-level Econometrics

A good measure of whether you’ll survive graduate school is enjoying and succeeding in the grad-level courses, Econometrics, and Financial Economics.  I took many of these “critical” courses as an undergraduate in Economics.  I squeezed the rest into my MBA schedule:  Multivariable Calculus, Linear Algebra, grad-level Price Theory, undergrad-level Applied Econometrics, and grad-level Econometrics.

Good to Have

  • Math – undergraduate-level Real Analysis (Stanford specifically mentions “analysis” in skills they want applicants to have)
  • Math – graduate-level Real Analysis
  • Math – Ordinary Differential Equations
  • Math – Partial Differential Equations
  • Math – Mathematical Finance
  • Math – graduate-level Advanced Probability
  • Econ – Game Theory
  • Econ – graduate-level Mathematical Economics
  • Stats – SAS Certification (see post “Ph.D. Prep – Stata vs. SAS”)
  • Stats – Statistical Computing
  • Stats – Statistical Theory
  • Stats – Stochastic Processes
  • Stats – Bayesian Methods
  • Stats – Time Series
  • Stats – Multivariate Analysis

The only course I took of these was Ordinary Differential Equations.  I will probably want to take more during my Ph.D.

Also, Consider a Minor

  • Economics
  • Statistics
  • Mathematics (I completed the equivalent of a Math Minor, but I won’t actually get it since it’s not part of the MBA program)
  • Computer Science

I added Computer Science to this list.  Many successful researchers are also proficient computer programmers, who are good at collecting and processing large quantities of data.  The most common programs/languages I hear are SAS, Stata, MatLab, and Python.