Quant Finance Master’s Guide 2021
Risk.net’s guide to the world’s leading quant master’s programmes, with the top 25 schools ranked
Jump to: All programmes; Methodology; How to read the tables
Welcome to the latest edition of Risk.net’s guide to the world’s leading quantitative finance master’s programmes, and ranking of the top 25 courses.
Forty-nine programmes feature in the 2021 edition of the guide, with the top 25 ranked in the table above according to Risk.net’s proprietary methodology (jump to Methodology). The table below shows all 49 programmes – click on an institution’s entry in the table to access its full listing, featuring all programme data and interviews with course directors. A full list of all entries can also be found at the bottom of this page.
US programmes continue their dominance: nine places in the top 25 are occupied by European courses, one more than last year – and one more than the positions representing New York City this year.
Still, the composition of the top 25 has shifted somewhat, in part due to the impact of the coronavirus. Some storied programmes, particularly those with large numbers of international students, have seen their ranking drop precipitously, as scores of applicants, having secured offers successfully, chose to defer their places until the start of the next academic year, often because of travel bans and regional restrictions – hurting a uni's percentage of offer holders enrolling score. Others submitted data too late to be ranked, while others failed to submit entirely.
Once again, the guide covers only master’s programmes in which the teaching of quantitative finance is central. Programmes whose focus is on other subjects – corporate finance, management or statistics – that may still feature quantitative finance courses have not been considered here. The list of programmes is non-exhaustive. Programmes that failed to provide updated statistics were not included in the 2021 edition.
We are grateful for the help of programme directors and faculty administrators when collecting data. Risk.net bears no responsibility for exceptions, oversights or omissions. We will gladly consider feedback in this regard.
The guide should not be relied on for advice – but we hope it proves helpful to would-be master’s students, their teachers and their future employers.
The table below lists all courses featured in the 2021 guide. These are not ordered by ranking, and by default are sorted alphabetically. Please click on an individual programme’s row to view its entry in the guide. If the table is not displaying properly, click here for a pop-out version.
Research and profiles: James Ryder. Ranking methodology: Mauro Cesa. Editing by Tom Osborn, Alex Krohn, Louise Marshall and Olesya Dmitracova
Ranking methodology
To compile the ranking of the top 25 programmes, we considered eight metrics. These have been standardised with respect to the total pool of entries, and a weight has been assigned to each to reflect their contribution to the final score. The total score is the sum of the eight standardised metrics. The institution with the highest score takes the top position in the ranking.
The methodology used for this year’s ranking is identical to that used for the 2020 guide. The eight variables and the respective weights are:
5% – Average class size;
10% – Acceptance rate;
10% – Percentage of offer-holders who enrol;
5% – Ratio between lecturers and students;
10% – Number of industry-affiliated lecturers over the total number of lecturers;
30% – Employment rate in finance sector six months after graduation;
5% – Number of citations for the five most cited lecturers in the past four years;
25% – Average salary six months after graduation, adjusted for the purchasing power conversion factor provided by the World Bank.
The average number of students per class and the programme’s acceptance rate – an indicator of the selectivity of a programme – contribute negatively to the final score, so, the lower they are, the higher its final score.
In order for an institution to be considered for this ranking, it needed to provide sufficient data for the calculation of the final score. Institutions that submitted insufficient data have not been considered.
Not all institutions provided the number of citations for their lecturers. Where possible, these figures were sourced from Google Scholar. Where that was not possible, the number of citations is considered as zero.
The ranking, as well as the guide, relies on the figures provided by the institutions to be accurate. Risk.net bears no responsibility for any inaccurate metrics, or their impact on a university’s position in the guide.
North America
Baruch College, City University of New York
Boston University (Questrom School of Business)
University of California, Berkeley (Haas School of Business)
University of California, Los Angeles (Anderson School of Management)
Carnegie Mellon University
University of Chicago
Columbia University
Columbia University (Columbia Engineering)
Cornell University
Fordham University
Georgia Institute of Technology
University of Illinois at Urbana-Champaign
Lehigh University
University of Minnesota
New York University (Courant Institute of Mathematical Sciences)
New York University (Tandon School of Engineering)
North Carolina State University
Princeton University (Bendheim Center for Finance)
Rutgers University
Stony Brook University
University of Washington
University of Toronto
University of Waterloo
Europe
Imperial College Business School
Imperial College London
King’s College London
University of Oxford
University of Warwick
University of York
EPFL
ETH Zurich/University of Zurich
University of St Gallen
Paris-Diderot University
Paris-Saclay University
Paris-Sorbonne University/Ecole Polytechnique
University of Bologna
Collegio Carlo Alberto, University of Turin
University of Florence
University of Amsterdam
Erasmus University Rotterdam
WU: Vienna University of Economics and Business
Technical University of Munich
Asia-Pacific
Monash University
University of Technology Sydney
City University of Hong Kong
Hong Kong University of Science and Technology
Chinese University of Hong Kong, Shenzhen
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