Recommended reading - MQF

The following information is presented for prospective students with two purposes in mind.

  • First, it outlines the appropriate background for applicants to the program.
  • Second, it can assist in preparation for the entrance test as well as the interview.

Background Textbooks

Finance

Probability and Statistics

Topics: Distribution of random variables; Conditional probability and stochastic independence; Some special distributions; Distributions of functions of random variables; Limiting distributions; Estimation; Markov Chains; and Brownian Motion and Markov Processes.

Econometrics

Familiarity with multiple regression would be helpful. Most standard econometric textbooks will cover this topic. For example:

Real Analysis

Topics: The real number system; Elements of set theory; Numerical sequences and series; Continuity; The Riemann integral; and Sequences and series of functions. These topics can be found in the classic textbooks by:

Calculus

Topics: Techniques of integration; Partial derivatives; Multiple integrals; Vector calculus; Optimization of multivariable functions; Ordinary differential equations. Most concepts in univariate differential and integral calculus will be used routinely. There are many books that cover these topics, for example:

Linear Algebra

Topics: Matrix algebra; Determinants; Vector spaces and linear transformation; Orthogonality and projections; and Eigenvalues and eigenvectors. There are many excellent textbooks on linear algebra that cover these topics, for example:

Additional readings

C++

C++ is a challenging programming language for novice quants. The books listed below, if read and understood properly, would help make you somewhat proficient in C++ programming:

Python

Python has become increasingly popular in the quantitative finance world. It is a relatively easy language to learn, but it is harder to master, because it has many useful libraries. Regardless of which type of quant you wish to become, it would a valuable asset to know Python, as it is only going to become more widely adopted in the financial industry as time goes on.

MATLAB

MatLab is still widely used in the financial industry. The following textbooks will help you learn, and upgrade your skill in, Matlab:

R

As with MatLab, R is extensively used in the financial industry as it is a natural language with which to carry out advanced statistical analysis for model vetting exercises and predictive analysis. A great way to learn R is to pair the following books with a course in statistics, which will often make use of R (such as STAT 974/ACTSC 974):

Excel/VBA

Although not having nearly as much computational horsepower as that of C++ or Python, Excel is still the most widely used software in the financial industry. The following textbooks could prove handy:

General Readings in Finance

If you feel that you lack basic financial markets knowledge, and cannot tell your stock from your bond, or your bank from your fund, then you should make these books your bedtime readings:

Interview Preparation Readings

In a highly competitive world, it is simply not good enough just to be aware of capital markets and how they function, the mathematics of derivatives pricing and quantitative trading methods, and being able to program in C++ and Python. You would also need to prepare yourselves to be successful in the interviews. The following books are some resources for helping you do this. Make sure that you study not only the content of the brainteasers, but also try to deconstruct how they are put together in the first place and what you are really being asked.

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